def data_gsm_unchecked(request):
'''Get the unchecked GSM data.
'''
platform = int(request.matchdict['id'])
if request.GET['format'] == 'geojson':
# Query
query = select([
DataGsm.id.label('id'),
DataGsm.lat,
DataGsm.lon,
DataGsm.date
]).where(DataGsm.platform_ == platform).where(DataGsm.checked == False
).order_by(desc(DataGsm.date)).limit(1000)
# Create list of features from query result
features = [
{
'type':'Feature',
'properties':{'date':str(date)},
'geometry':{'type':'Point', 'coordinates':[float(lon),float(lat)]},
'id':id_
}
for id_, lat, lon, date in DBSession.execute(query).fetchall()]
transaction.commit()
result = {'type':'FeatureCollection', 'features':features}
return result
elif request.GET['format'] == 'json':
# Query
query = select([
DataGsm.id.label('id'),
DataGsm.lat.label('lat'),
DataGsm.lon.label('lon'),
DataGsm.ele.label('ele'),
DataGsm.date.label('date')]
).where(DataGsm.platform_ == platform
).where(DataGsm.checked == False
).order_by(desc(DataGsm.date)).limit(1000)
data = DBSession.execute(query).fetchall()
# Load data from the DB then
# compute the distance between 2 consecutive points.
df = pd.DataFrame.from_records(data, columns=data[0].keys(), coerce_float=True)
X1 = df.iloc[:-1][['lat', 'lon']].values
X2 = df.iloc[1:][['lat', 'lon']].values
df['dist'] = np.append(haversine(X1, X2), 0).round(3)
# Compute the speed
df['speed'] = (df['dist']/((df['date']-df['date'].shift(-1)).fillna(1)/np.timedelta64(1, 'h'))).round(3)
# Values to import : the first per hour
ids = df.set_index('date').resample('1H', how='first').dropna().id.values
df['import'] = df.id.isin(ids)
df['date'] = df['date'].apply(str)
# Fill NaN
df.fillna(value={'ele':-999}, inplace=True)
return df.to_dict('records')
def argos_unchecked(request):
"""Returns list of unchecked locations for a given ptt."""
# ptt is a mandatory parameter.
try:
ptt = int(request.GET['ptt'])
except:
raise HTTPBadRequest()
# Get all unchecked data for this ptt and this individual
# Type 0 = Argos data, type 1 = GPS data
argos_data = select([
Argos.pk.label('pk'),
Argos.date,
Argos.lat,
Argos.lon,
Argos.lc,
literal_column('0').label('type')
]).where(Argos.checked == False).where(Argos.ptt == ptt)
gps_data = select([
Gps.pk.label('pk'),
Gps.date,
Gps.lat,
Gps.lon,
literal_column('NULL').label('lc'),
literal_column('1').label('type')
]).where(Gps.checked == False).where(Gps.ptt == ptt)
unchecked = union(argos_data, gps_data).alias('unchecked')
# ind_id is a facultative parameter
try:
ind_id = int(request.GET['ind_id'])
all_data = select([
unchecked.c.pk,
unchecked.c.date,
unchecked.c.lat,
unchecked.c.lon,
unchecked.c.lc,
unchecked.c.type
]).select_from(unchecked
.join(SatTrx, SatTrx.ptt == ptt)
.join(ProtocolIndividualEquipment,
and_(
SatTrx.id == ProtocolIndividualEquipment.sat_id,
unchecked.c.date >= ProtocolIndividualEquipment.begin_date,
or_(
unchecked.c.date < ProtocolIndividualEquipment.end_date,
ProtocolIndividualEquipment.end_date == None
)
)
)
).where(ProtocolIndividualEquipment.ind_id == ind_id)
except KeyError or TypeError:
all_data = select([
unchecked.c.pk,
unchecked.c.date,
unchecked.c.lat,
unchecked.c.lon,
unchecked.c.lc,
unchecked.c.type
])
ind_id = None
# Initialize json object
result = {'ptt':{}, 'locations':[], 'indiv':{}}
# Load data from the DB then
# compute the distance between 2 consecutive points.
data = DBSession.execute(all_data.order_by(desc(all_data.c.date))).fetchall()
df = pd.DataFrame.from_records(data, columns=data[0].keys(), coerce_float=True)
X1 = df.ix[:,['lat', 'lon']].values[:-1,:]
X2 = df.ix[1:,['lat', 'lon']].values
dist = pd.Series(np.append(haversine(X1, X2).round(3), 0), name='dist')
df = pd.concat([df['pk'], df['date'].apply(lambda x: str(x)), df['lat'], df['lon'], df['lc'], df['type'], dist], axis=1)
result['locations'] = df.to_dict('records')
# Get informations for this ptt
ptt_infos = select([SatTrx.ptt, SatTrx.manufacturer, SatTrx.model]).where(SatTrx.ptt == ptt)
result['ptt']['ptt'], result['ptt']['manufacturer'], result['ptt']['model'] = DBSession.execute(ptt_infos).fetchone()
# Get informations for the individual
if ind_id is not None:
query = select([
Individual.id.label('id'),
Individual.age.label('age'),
Individual.sex.label('sex'),
Individual.specie.label('specie'),
Individual.monitoring_status.label('monitoring_status'),
Individual.origin.label('origin'),
Individual.survey_type.label('survey_type')
]).where(Individual.id == ind_id)
result['indiv'] = dict(DBSession.execute(query).fetchone())
# Last known location
c = V_Individuals_LatLonDate.c
query = select([c.lat, c.lon, c.date]).where(c.ind_id == ind_id).order_by(desc(c.date)).limit(1)
lat, lon, date = DBSession.execute(query).fetchone()
result['indiv']['last_loc'] = {'date':str(date), 'lat':float(lat), 'lon':float(lon)}
return result