def parser(platform_info, sensor_info, lines):
"""
Parse and assign sponge data from XML file.
"""
_data = Data(''.join(lines))
# Each Device tag represents a time sample.
num_samples = len(_data.devices)
data = {
'dt' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'time' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'pdt' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'ptime' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'ds' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'session' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'pds' : n.array(n.ones((num_samples,)) * n.nan, dtype=object),
'psession' : n.array(n.ones((num_samples,)) * n.nan, dtype=long),
'record' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
'status' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
'pstatus' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
'abs_speed' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'direction' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'v' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'u' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'heading' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'tiltx' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'tilty' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'std_speed' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'strength' : n.array(n.ones((num_samples,)) * n.nan, dtype=float),
'pings' : n.array(n.ones((num_samples,)) * n.nan, dtype=int),
}
for (sample_index, sample) in enumerate(_data.devices):
# sample time at the platform
dt = {'month' : int(sample['time'][5:7]),
'day' : int(sample['time'][8:10]),
'year' : int(sample['time'][0:4]),
'hour' : int(sample['time'][11:13]),
'min' : int(sample['time'][14:16]),
'sec' : int(sample['time'][17:19]),
}
dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d:%(sec)02d' \
% dt
dt = procutil.scanf_datetime(dt, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
dt = dt + datetime.timedelta(hours=sensor_info['utc_offset'])
data['dt'][sample_index] = dt
data['time'][sample_index] = procutil.dt2es(dt)
# sample time at the package
package_dt = {'month' : int(sample['data_time'][5:7]),
'day' : int(sample['data_time'][8:10]),
'year' : int(sample['data_time'][0:4]),
'hour' : int(sample['data_time'][11:13]),
'min' : int(sample['data_time'][14:16]),
'sec' : int(sample['data_time'][17:19]),
}
package_dt = ('%(month)02d-%(day)02d-%(year)04d ' +
'%(hour)02d:%(min)02d:%(sec)02d') \
% package_dt
package_dt = procutil.scanf_datetime(package_dt, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
package_dt = package_dt + \
datetime.timedelta(hours=sensor_info['utc_offset'])
data['pdt'][sample_index] = package_dt
data['ptime'][sample_index] = procutil.dt2es(package_dt)
# platform session time
ds = {'month' : int(sample['sessionid'][14:16]),
'day' : int(sample['sessionid'][17:19]),
'year' : int(sample['sessionid'][9:13]),
'hour' : int(sample['sessionid'][20:22]),
'min' : int(sample['sessionid'][23:25]),
'sec' : int(sample['sessionid'][26:28]),
}
ds = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d:%(sec)02d' \
% ds
ds = procutil.scanf_datetime(ds, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
ds = ds + datetime.timedelta(hours=sensor_info['utc_offset'])
data['ds'][sample_index] = ds
data['session'][sample_index] = procutil.dt2es(ds)
# package session time
package_ds = {'month' : int(sample['data_sessionid'][5:7]),
'day' : int(sample['data_sessionid'][8:10]),
'year' : int(sample['data_sessionid'][0:4]),
'hour' : int(sample['data_sessionid'][11:13]),
'min' : int(sample['data_sessionid'][14:16]),
'sec' : int(sample['data_sessionid'][17:19]),
}
package_ds = ('%(month)02d-%(day)02d-%(year)04d ' +
'%(hour)02d:%(min)02d:%(sec)02d') \
% package_ds
package_ds = procutil.scanf_datetime(package_ds, fmt='%m-%d-%Y %H:%M:%S')
if sensor_info['utc_offset']:
package_ds = package_ds + \
datetime.timedelta(hours=sensor_info['utc_offset'])
data['pds'][sample_index] = package_ds
data['psession'][sample_index] = procutil.dt2es(package_ds)
# platform variables
try:
data['record'][sample_index] = int(sample["recordnumber"])
except KeyError:
pass
try:
data['status'][sample_index] = int(sample["status"].
partition(":")[0])
except (KeyError, AttributeError, ):
pass
# package variables
try:
data['pstatus'][sample_index] = int(sample.sensors
[sensor_info["id_number"]]
["status"].
partition(":")[0])
except (KeyError, AttributeError, ):
pass
try:
data['abs_speed'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["abs_speed_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['direction'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["direction_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['v'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["v_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['u'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["u_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['heading'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["heading_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['tiltx'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["tiltx_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['tilty'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["tilty_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['std_speed'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["std_speed_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['strength'][sample_index] = float(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["strength_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
try:
data['pings'][sample_index] = int(sample.sensors
[sensor_info["id_number"]].
points[sensor_info
["pings_description"]]
["value"])
except (KeyError, AttributeError, ):
pass
return data
def parser(platform_info, sensor_info, lines):
"""
Parse and assign wind profile data from main Sodar file.
"""
main_data = maindata.MainData(''.join(lines))
num_profiles = len(main_data)
min_altitude = sensor_info['min_altitude']
altitude_interval = sensor_info['altitude_interval']
num_altitudes = sensor_info['num_altitudes']
sensor_elevation = sensor_info['sensor_elevation']
altitudes = [(altitude_num * altitude_interval) + min_altitude
for altitude_num in range(num_altitudes)]
elevations = [altitude + sensor_elevation for altitude in altitudes]
data = {
'dt' : n.array(n.ones((num_profiles,), dtype=object) * n.nan),
'time' : n.array(n.ones((num_profiles,), dtype=long) * n.nan),
'z' : n.array(elevations, dtype=float),
'u' : n.array(n.ones((num_profiles,
num_altitudes), dtype=float) * n.nan),
'v' : n.array(n.ones((num_profiles,
num_altitudes), dtype=float) * n.nan),
}
gaps = {}
for variable in main_data.variables:
symbol = variable['symbol']
gaps[symbol] = variable['gap']
if symbol not in manual:
data[symbol.lower()] = n.array(n.ones((num_profiles,
num_altitudes),
dtype=float) * n.nan)
data['error'] = n.array(n.ones((num_profiles,
num_altitudes), dtype = int) * n.nan)
for (profile_index, profile) in enumerate(main_data):
dt = {'month' : profile.stop.month,
'day' : profile.stop.day,
'year' : profile.stop.year,
'hour' : profile.stop.hour,
'min' : profile.stop.minute,
}
dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%(min)02d' % dt
dt = procutil.scanf_datetime(dt, fmt='%m-%d-%Y %H:%M')
if sensor_info['utc_offset']:
dt = dt + datetime.timedelta(hours=sensor_info['utc_offset'])
data['dt'][profile_index] = dt
data['time'][profile_index] = procutil.dt2es(dt)
for (observation_index, observation) in enumerate(profile):
radial = observation['speed']
theta = observation['dir']
if radial != gaps['speed'] and theta != gaps['dir']:
theta = math.pi * float(theta) / 180.0
radial = float(radial)
data['u'][profile_index][observation_index] = \
-radial * math.sin(theta)
data['v'][profile_index][observation_index] = \
-radial * math.cos(theta)
for variable in profile.variables:
if variable not in manual and \
observation[variable] != gaps[variable]:
data[variable.lower()][profile_index][observation_index] = \
float(observation[variable])
data['error'][profile_index][observation_index] = \
int(observation['error'])
return data