def __init__(self, stationid, longitude, latitude):
self.id = stationid
self.lon = longitude
self.lat = latitude
self.data = DynamicRecArray(dtype={'names': OUTPUT_NAMES,
'formats':OUTPUT_TYPES})
def process_timeseries(config_file):
"""
Process a set of timeseries files to include the multiplier values.
The combined multiplier values are stored in a shape file as fields,
and records are keyed by the same code that is used to select
stations for sampling.
:param str config_file: Path to a configuration file.
"""
config = ConfigParser()
config.read(config_file)
stnFile = config.get('Input', 'LocationFile')
key_name = config.get('Timeseries', 'StationID')
inputPath = pjoin(config.get('Output', 'Path'), 'process', 'timeseries')
outputPath = pjoin(inputPath, 'local')
if not isdir(outputPath):
try:
os.makedirs(outputPath)
except OSError:
raise
log.info("Loading stations from %s" % stnFile)
log.info("Timeseries data will be written into %s" % outputPath)
directions = ['n', 'ne', 'e', 'se', 's', 'sw', 'w', 'nw']
sf = shapefile.Reader(stnFile)
field_names = [sf.fields[i][0] for i in range(1, len(sf.fields))]
try:
key_index = field_names.index(key_name)
except ValueError:
log.exception("{0} not a field in {1}".format(key_name, stnFile))
raise
min_data = DynamicRecArray(dtype={
'names': MINMAX_NAMES,
'formats': MINMAX_TYPES
})
max_data = DynamicRecArray(dtype={
'names': MINMAX_NAMES,
'formats': MINMAX_TYPES
})
records = sf.records()
indexes = []
for dir in directions:
fieldname = 'm4_%s' % dir
indexes.append(field_names.index(fieldname))
for record in records:
stnId = record[key_index]
inputFile = pjoin(inputPath, 'ts.{0}.csv'.format(stnId))
outputFile = pjoin(outputPath, 'ts.{0}.csv'.format(stnId))
if os.path.isfile(inputFile):
# Load multipliers for this location:
mvals = [float(record[i]) for i in indexes]
maxdata, mindata = tsmultiply(inputFile, tuple(mvals), outputFile)
min_data.append(tuple(mindata))
max_data.append(tuple(maxdata))
else:
log.debug("No timeseries file for {0}".format(stnId))
# Save local minima/maxima
maxfile = pjoin(outputPath, 'local_maxima.csv')
minfile = pjoin(outputPath, 'local_minima.csv')
maxheader = ('Station,Time,Longitude,Latitude,Speed,'
'UU,VV,Bearing,Pressure')
np.savetxt(maxfile,
max_data.data,
fmt=MINMAX_FMT,
delimiter=',',
header=maxheader,
comments='')
np.savetxt(minfile,
min_data.data,
fmt=MINMAX_FMT,
delimiter=',',
header=maxheader,
comments='')
def process_timeseries(config_file):
"""
Process a set of timeseries files to include the multiplier values.
The combined multiplier values are stored in a shape file as fields,
and records are keyed by the same code that is used to select
stations for sampling.
:param str config_file: Path to a configuration file.
"""
config = ConfigParser()
config.read(config_file)
stnFile = config.get('Timeseries', 'StationFile')
key_name = config.get('Timeseries', 'StationID')
inputPath = pjoin(config.get('Output', 'Path'),
'process', 'timeseries')
outputPath = pjoin(inputPath, 'local')
if not isdir(outputPath):
try:
os.makedirs(outputPath)
except OSError:
raise
log.info("Loading stations from %s"%stnFile)
log.info("Timeseries data will be written into %s"%outputPath)
directions = ['n', 'ne', 'e', 'se', 's', 'sw', 'w', 'nw']
sf = shapefile.Reader(stnFile)
field_names = [sf.fields[i][0] for i in range(1, len(sf.fields))]
try:
key_index = field_names.index(key_name)
except ValueError:
log.exception("{0} not a field in {1}".format(key_name, stnFile))
raise
min_data = DynamicRecArray(dtype={'names': MINMAX_NAMES,
'formats': MINMAX_TYPES})
max_data = DynamicRecArray(dtype={'names': MINMAX_NAMES,
'formats': MINMAX_TYPES})
records = sf.records()
indexes = []
for dir in directions:
fieldname = 'm4_%s' % dir
indexes.append(field_names.index(fieldname))
for record in records:
stnId = record[key_index]
inputFile = pjoin(inputPath, 'ts.{0}.csv'.format(stnId))
outputFile = pjoin(outputPath, 'ts.{0}.csv'.format(stnId))
if os.path.isfile(inputFile):
# Load multipliers for this location:
mvals = [float(record[i]) for i in indexes]
maxdata, mindata = tsmultiply(inputFile, tuple(mvals), outputFile)
min_data.append(tuple(mindata))
max_data.append(tuple(maxdata))
else:
log.debug("No timeseries file for {0}".format(stnId))
# Save local minima/maxima
maxfile = pjoin(outputPath, 'local_maxima.csv')
minfile = pjoin(outputPath, 'local_minima.csv')
maxheader = ('Station,Time,Longitude,Latitude,Speed,'
'UU,VV,Bearing,Pressure')
np.savetxt(maxfile, max_data.data, fmt=MINMAX_FMT, delimiter=',',
header=maxheader, comments='')
np.savetxt(minfile, min_data.data, fmt=MINMAX_FMT, delimiter=',',
header=maxheader, comments='')
def shutdown(self):
"""
Write the data to file, each station to a separate file.
"""
header = 'Station,Time,Longitude,Latitude,Speed,UU,VV,Bearing,Pressure'
maxheader = ('Station,Time,Longitude,Latitude,Speed,'
'UU,VV,Bearing,Pressure')
max_data = DynamicRecArray(dtype={
'names': MINMAX_NAMES,
'formats': MINMAX_TYPES
})
min_data = DynamicRecArray(dtype={
'names': MINMAX_NAMES,
'formats': MINMAX_TYPES
})
for stn in self.stations:
if np.any(stn.data.data['Speed'] > 0.0):
fname = pjoin(self.outputPath, 'ts.%s.csv' % str(stn.id))
np.savetxt(fname,
np.array(stn.data.data),
fmt=OUTPUT_FMT,
delimiter=',',
header=header,
comments='')
max_step = np.argmax(stn.data.data['Speed'])
min_step = np.argmin(stn.data.data['Pressure'])
max_data.append(tuple(stn.data.data[max_step]))
min_data.append(tuple(stn.data.data[min_step]))
np.savetxt(self.maxfile,
max_data.data,
fmt=MINMAX_FMT,
delimiter=',',
header=maxheader,
comments='')
np.savetxt(self.minfile,
min_data.data,
fmt=MINMAX_FMT,
delimiter=',',
header=maxheader,
comments='')
"""
for stn in self.stations:
if type(self.maxdata[stn.id][3]) == datetime.datetime:
self.maxdata[stn.id][3] = self.maxdata[stn.id][3].strftime(ISO_FORMAT)
self.mindata[stn.id][3] = self.mindata[stn.id][3].strftime(ISO_FORMAT)
self.maxdata[stn.id][0] = str(int(stn.id))
self.mindata[stn.id][0] = str(int(stn.id))
maxdata.append(self.maxdata[stn.id])
mindata.append(self.mindata[stn.id])
for stn in self.stnid:
if type(self.maxdata[stn][3]) == datetime.datetime:
self.maxdata[stn][3] = self.maxdata[stn][3].strftime(ISO_FORMAT)
self.mindata[stn][3] = self.mindata[stn][3].strftime(ISO_FORMAT)
self.maxdata[stn][0] = str(int(stn))
self.mindata[stn][0] = str(int(stn))
maxdata.append(self.maxdata[stn])
mindata.append(self.mindata[stn])
else:
pass
np.savetxt(maxfname, np.array(maxdata), fmt='%s',
header=maxheader, delimiter=',')
#['%s','%7.3f','%7.3f','%s','%6.2f','%6.2f',
# '%6.2f','%6.2f','%7.2f'] )
minfname = pjoin(self.outputPath, 'minpressure.csv')
np.savetxt(minfname, np.array(mindata), fmt='%s',
header=maxheader, delimiter=',')
#['%s','%7.3f','%7.3f','%s','%6.2f','%6.2f',
# '%6.2f','%6.2f','%7.2f'] )
"""
log.info("Station data written to file")
def shutdown(self):
"""
Write the data to file, each station to a separate file.
"""
header = 'Station,Time,Longitude,Latitude,Speed,UU,VV,Bearing,Pressure'
maxheader = ('Station,Time,Longitude,Latitude,Speed,'
'UU,VV,Bearing,Pressure')
max_data = DynamicRecArray(dtype={'names': MINMAX_NAMES,
'formats':MINMAX_TYPES})
min_data = DynamicRecArray(dtype={'names': MINMAX_NAMES,
'formats':MINMAX_TYPES})
for stn in self.stations:
if np.any(stn.data.data['Speed'] > 0.0):
fname = pjoin(self.outputPath, 'ts.%s.csv' % str(stn.id))
np.savetxt(fname, np.array(stn.data.data), fmt=OUTPUT_FMT,
delimiter=',', header=header, comments='')
max_step = np.argmax(stn.data.data['Speed'])
min_step = np.argmin(stn.data.data['Pressure'])
max_data.append(tuple(stn.data.data[max_step]))
min_data.append(tuple(stn.data.data[min_step]))
np.savetxt(self.maxfile, max_data.data, fmt=MINMAX_FMT, delimiter=',',
header=maxheader, comments='')
np.savetxt(self.minfile, min_data.data, fmt=MINMAX_FMT, delimiter=',',
header=maxheader, comments='')
"""
for stn in self.stations:
if type(self.maxdata[stn.id][3]) == datetime.datetime:
self.maxdata[stn.id][3] = self.maxdata[stn.id][3].strftime(ISO_FORMAT)
self.mindata[stn.id][3] = self.mindata[stn.id][3].strftime(ISO_FORMAT)
self.maxdata[stn.id][0] = str(int(stn.id))
self.mindata[stn.id][0] = str(int(stn.id))
maxdata.append(self.maxdata[stn.id])
mindata.append(self.mindata[stn.id])
for stn in self.stnid:
if type(self.maxdata[stn][3]) == datetime.datetime:
self.maxdata[stn][3] = self.maxdata[stn][3].strftime(ISO_FORMAT)
self.mindata[stn][3] = self.mindata[stn][3].strftime(ISO_FORMAT)
self.maxdata[stn][0] = str(int(stn))
self.mindata[stn][0] = str(int(stn))
maxdata.append(self.maxdata[stn])
mindata.append(self.mindata[stn])
else:
pass
np.savetxt(maxfname, np.array(maxdata), fmt='%s',
header=maxheader, delimiter=',')
#['%s','%7.3f','%7.3f','%s','%6.2f','%6.2f',
# '%6.2f','%6.2f','%7.2f'] )
minfname = pjoin(self.outputPath, 'minpressure.csv')
np.savetxt(minfname, np.array(mindata), fmt='%s',
header=maxheader, delimiter=',')
#['%s','%7.3f','%7.3f','%s','%6.2f','%6.2f',
# '%6.2f','%6.2f','%7.2f'] )
"""
log.info("Station data written to file")
