def get_sst_filename(config):
base_filename = config['sst_filename']
sst_time = get_sst_time(config)
sst_filename = sub_date(base_filename, init_time=sst_time)
return sst_filename
def main():
# merge command-line and file-specified arguments
config = conf.config(__doc__, sys.argv[1:])
logger = loghelper.create(LOGGER,
log_level=config.get('log.level'),
log_fmt=config.get('log.format'))
if config.get('log.file'):
log_file = config['log.file']
logger.addHandler(
loghelper.file_handler(log_file, config['log.level'],
config['log.format']))
logger.debug('now logging to file')
dry_run = config.get('dry-run')
# either the start time is exactly specified, or else we calculate it
if config.get('start'):
init_time = config['start']
else:
init_time = shared.get_time(base_time=config.get('base-time'),
delay=config.get('delay'),
round=config.get('cycles'))
if config.get('end'):
end_init = config['end']
init_interval = config['init_interval']
init_times = list(
rrule.rrule(freq=rrule.HOURLY,
interval=init_interval,
dtstart=init_time,
until=end_init))
else:
init_times = [init_time]
for init_time in init_times:
# one-argument function to do initial-time substitution in strings
expand = lambda s: substitute.sub_date(s, init_time=init_time) if type(
s) == type("") else s
# dictionary of replacements e.g. %iY : 2015
date_replacements = substitute.date_replacements(init_time=init_time)
source = expand(config['source'])
target = expand(config['target'])
assert (_are_compatible(source, target))
_recursive_replace(source, target, date_replacements)
def get_bdy_filenames(grb_fmt, bdy_times):
""" Creates a list of boundary conditions filenames
based on the information in config. In general this will
be called once per forecast, so there is only one
init_time.
Arguments:
config -- dictionary containing various configuration options
"""
logger = get_logger()
logger.debug('*** GENERATING BOUNDARY CONDITION FILENAMES ***')
filelist = [sub_date(grb_fmt, init_time=bdy_times[0], valid_time=b) for b in bdy_times]
logger.debug(filelist)
return filelist
def get_bdy_filenames(grb_fmt, bdy_times):
""" Creates a list of boundary conditions filenames
based on the information in config. In general this will
be called once per forecast, so there is only one
init_time.
Arguments:
config -- dictionary containing various configuration options
"""
logger = get_logger()
logger.debug('*** GENERATING BOUNDARY CONDITION FILENAMES ***')
filelist = [sub_date(grb_fmt, init_time=bdy_times[0], valid_time=b) for b in bdy_times]
logger.debug(filelist)
return filelist
def main():
# merge command-line and file-specified arguments
config = conf.config(__doc__, sys.argv[1:])
logger = loghelper.create(LOGGER, log_level=config.get('log.level'), log_fmt=config.get('log.format'))
if config.get('log.file'):
log_file = config['log.file']
logger.addHandler(loghelper.file_handler(log_file, config['log.level'], config['log.format']))
logger.debug('now logging to file')
dry_run = config.get('dry-run')
# either the start time is exactly specified, or else we calculate it
if config.get('start'):
init_time = config['start']
else:
init_time = shared.get_time(base_time=config.get('base-time'), delay=config.get('delay'), round=config.get('cycles'))
if config.get('end'):
end_init = config['end']
init_interval = config['init_interval']
init_times = list(rrule.rrule(freq=rrule.HOURLY, interval=init_interval, dtstart=init_time, until=end_init))
else:
init_times = [init_time]
for init_time in init_times:
# one-argument function to do initial-time substitution in strings
expand = lambda s : substitute.sub_date(s, init_time=init_time) if type(s)==type("") else s
# dictionary of replacements e.g. %iY : 2015
date_replacements = substitute.date_replacements(init_time=init_time)
source = expand(config['source'])
target = expand(config['target'])
assert(_are_compatible(source,target))
_recursive_replace(source, target, date_replacements)
def ncdump(config):
logger = loghelper.get(LOGGER)
# _listify ensures arguments are enclosed within a list
# to simplify treatement in following code
files = nctools._listify(config['<files>'])
vars = nctools._listify(config.get('vars'))
global_atts = nctools._listify(config.get('global-atts'))
var_atts = nctools._listify(config.get('var-atts'))
coord_vars = nctools._listify(config.get('coords'))
sort_by = nctools._listify(config.get('sort-by'))
order_by = nctools._listify(config.get('order-by'))
out = config.get('out')
pivot = config.get('pivot')
valid_time = config.get('valid-time')
format = config.get('format')
filter = config.get('filter')
split_by = config.get('split-by')
concat = config.get('concat')
start = config.get('start')
delay = config.get('delay')
cycles = nctools._listify(config.get('cycles'))
basetime = start if start else datetime.datetime.today()
prior = _prior_time(basetime, delay=delay, hours=cycles)
logger.debug("using %s as a start time" % prior)
if files == []:
logger.info("no files specified, finding using options")
file_pattern = config.get('file-pattern')
if not file_pattern:
raise nctools.ConfigError(
'either supply files or specify file-pattern')
expanded = substitute.sub_date(file_pattern, init_time=prior)
files = glob.glob(expanded)
if files == []: raise IOError("no files found")
frame = nctools.melt(files,
vars,
global_atts,
var_atts,
coord_vars,
missing=MISSING_ATTS)
if valid_time:
logger.debug("adding valid time into frame")
frame['valid_time'] = frame[
'reftime'] + frame['leadtime'] * datetime.timedelta(0, 60 * 60)
if filter:
frame = nctools.filter(frame, filter)
if concat:
nctools.concat(frame, concat, name='variable', inplace=True)
if pivot:
frame = pd.pivot_table(frame,
index=['reftime', 'leadtime', 'location'],
columns='variable',
values='value')
frame.reset_index(inplace=True)
if sort_by: frame.sort(sort_by, inplace=True)
if order_by:
frame = frame[order_by]
if out:
out = substitute.sub_date(out, init_time=prior)
if split_by:
gb = frame.groupby(split_by)
for key, group in gb:
if out:
new_name = _merge_name(out, key)
save(gb.get_group(key),
new_name,
config['format'],
float_format=config.get('float-format'))
else:
print gb.get_group(key).to_string()
print '\n\n\n'
elif out:
save(frame,
out,
config['format'],
float_format=config.get('float-format'))
else:
print frame.to_string()
def power(config):
"""Reads 'time series' from netcdf time series file, and adds power as a variable. """
if __name__ == "__main__":
logger = loghelper.create_logger(config)
else:
logger = loghelper.get_logger(config['log.name'])
# listify ensures they are returned as a list, even if it is one file
files = shared._listify(config['<files>'])
# Number of samples to use should be in here
# Whether to normalise power should be in here
start = config.get('start')
delay = config.get('delay')
cycles = shared._listify(config.get('cycles'))
pnorm = config.get('pnorm')
pdist = config.get('pdist')
sstd = config.get('sstd')
dstd = config.get('dstd')
pquants = config.get('pquants')
quantiles = np.array(pquants)
pcurve_dir = config.get('pcurve-dir')
ts_dir = config.get('tseries-dir')
out = config.get('out')
metadata = config.get('metadata')
basetime = start if start else datetime.datetime.today()
prior = shared._prior_time(basetime, delay=delay, hours=cycles)
logger.debug("using %s as a start time" % prior)
if not files:
logger.debug("no files specified, finding using options")
file_pattern = config.get('file-pattern')
if not file_pattern: raise ConfigError('either supply files or specify file-pattern')
expanded = substitute.sub_date(file_pattern, init_time=prior)
files = glob.glob(expanded)
print("hello")
logger.debug(files)
# if we get to this point and there are still no files, then we have a problem
if not files: raise IOError("no files found")
logger.debug("input files: ")
logger.debug(files)
for f in files:
logger.debug("\t%s" % f)
# if pdist
if pdist: n=pdist
#grid_id = config['grid_id']
out_pattern = config.get('out')
for tseries_file in files:
dataset_in = Dataset(tseries_file, 'a')
# Get dimensions
dims = dataset_in.dimensions
nreftime = len(dims['reftime'])
ntime = len(dims['leadtime'])
nloc = len(dims['location'])
nheight = len(dims['height'])
loc_str_len = len(dims['loc_str_length'])
# Get coordinate variables
reftime = dataset_in.variables['reftime']
leadtime = dataset_in.variables['leadtime']
validtime = nctools._valid_time(reftime, leadtime)
refdt = num2date(reftime[:], reftime.units)
power_file = substitute.sub_date(out, init_time=refdt[0])
logger.info('Estimating power from time series: %s ' % tseries_file)
logger.info('Writing power time series to: %s ' % power_file)
location = [''.join(l.filled(' ')).strip() for l in dataset_in.variables['location']]
height = dataset_in.variables['height']
if power_file == tseries_file:
dataset_out = dataset_in
else:
dataset_out = Dataset(power_file, 'w')
# Get number of quantiles
nq = len(quantiles)
pdata = np.ma.zeros((ntime,nloc,nheight,nq+1), np.float) # mean will be 1st value
use_locs = []
# loop through locations and look for power-curve file
for l,loc in enumerate(location):
pcurve_file = '%s/%s.csv' %(pcurve_dir, loc)
# mask power data if no power curve found for this park
if not os.path.exists(pcurve_file):
#logger.debug("Power curve: %s not found, skipping" % pcurve_file)
pdata[:,l,:,:] = np.ma.masked
continue
logger.info('Predicting power output for %s' % loc )
#
# Open power curve
#
use_locs.append(l)
pcurve = from_file(pcurve_file)
for h in range(nheight):
speed = dataset_in.variables['SPEED'][0,:,l,h]
direction = dataset_in.variables['DIRECTION'][0,:,l,h]
#pwr = pcurve.power(speed,direction)
# pdist will create a distribution for each timetep based on sampling
# n times from a normal distribution.
pdist = pcurve.power_dist(speed, direction, sstd=sstd,dstd=dstd,n=n, normalise=pnorm)
pmean = np.mean(pdist, axis=1)
pquants = scipy.stats.mstats.mquantiles(pdist, prob=quantiles/100.0,axis=1, alphap=0.5, betap=0.5)
pdata[:,l,h,0] = pmean
pdata[:,l,h,1:] = pquants[:,:]
#logger.info('finished %s' % loc)
use_inds = np.array(use_locs)
if dataset_out != dataset_in:
dataset_out.createDimension('reftime', None)
dataset_out.createVariable('reftime', 'float', ('reftime',))
dataset_out.variables['reftime'][:] = reftime[:]
dataset_out.variables['reftime'].units = reftime.units
dataset_out.variables['reftime'].calendar = reftime.calendar
dataset_out.variables['reftime'].long_name = reftime.long_name
dataset_out.variables['reftime'].standard_name = reftime.standard_name
dataset_out.createDimension('leadtime', len(leadtime))
dataset_out.createVariable('leadtime', 'int', ('leadtime',))
dataset_out.variables['leadtime'][:] = leadtime[:]
dataset_out.variables['leadtime'].units = leadtime.units
dataset_out.variables['leadtime'].long_name = leadtime.long_name
dataset_out.variables['leadtime'].standard_name = leadtime.standard_name
dataset_out.createDimension('location', len(use_locs))
dataset_out.createDimension('loc_str_length', loc_str_len)
loc_data =np.array([list(l.ljust(loc_str_len, ' ')) for l in location])
dataset_out.createVariable('location', 'c', ('location', 'loc_str_length'))
dataset_out.variables['location'][:] = loc_data[use_inds,:]
dataset_out.createDimension('height', nheight)
dataset_out.createVariable('height', 'i', ('height',))
dataset_out.variables['height'][:] = height[:]
dataset_out.GRID_ID = dataset_in.GRID_ID
dataset_out.DX = dataset_in.DX
dataset_out.DY = dataset_in.DY
try:
dataset_out.variables['height'].units = height.units
except Exception:
logger.warn("height units missing")
pdata = pdata[:, use_inds, :, :]
for key in metadata.keys():
key = key.upper()
dataset_out.setncattr(key,dataset_in.getncattr(key))
pavg = dataset_out.createVariable('POWER','f',('reftime','leadtime','location','height'))
pavg.units = 'kW'
pavg.description = 'forecast power output'
pavg[0,:,:,:] = pdata[:,:,:,0]
for q, qval in enumerate(quantiles):
varname = 'POWER.P%02d' % qval
var = dataset_out.createVariable(varname,'f',('reftime','leadtime','location','height'))
if pnorm:
var.units = 'ratio'
else:
var.units = 'kW'
var.description = 'forecast power output'
var[0,:,:,:] = pdata[:,:,:,q+1]
#logger.debug(dataset_out)
dataset_in.close()
if dataset_out!=dataset_in:
dataset_out.close()
def ncdump(config):
logger = loghelper.get(LOGGER)
# _listify ensures arguments are enclosed within a list
# to simplify treatement in following code
files = nctools._listify(config['<files>'])
vars = nctools._listify(config.get('vars'))
global_atts = nctools._listify(config.get('global-atts'))
var_atts = nctools._listify(config.get('var-atts'))
coord_vars = nctools._listify(config.get('coords'))
sort_by = nctools._listify(config.get('sort-by'))
order_by = nctools._listify(config.get('order-by'))
out = config.get('out')
pivot = config.get('pivot')
valid_time = config.get('valid-time')
format = config.get('format')
filter = config.get('filter')
split_by = config.get('split-by')
concat = config.get('concat')
start = config.get('start')
delay = config.get('delay')
cycles = nctools._listify(config.get('cycles'))
basetime = start if start else datetime.datetime.today()
prior = _prior_time(basetime, delay=delay, hours=cycles)
logger.debug("using %s as a start time" % prior)
if files==[]:
logger.info("no files specified, finding using options")
file_pattern = config.get('file-pattern')
if not file_pattern: raise nctools.ConfigError('either supply files or specify file-pattern')
expanded = substitute.sub_date(file_pattern, init_time=prior)
files = glob.glob(expanded)
if files==[]: raise IOError("no files found")
frame = nctools.melt(files, vars, global_atts, var_atts, coord_vars, missing=MISSING_ATTS)
if valid_time:
logger.debug("adding valid time into frame")
frame['valid_time'] = frame['reftime'] + frame['leadtime']*datetime.timedelta(0,60*60)
if filter:
frame = nctools.filter(frame, filter)
if concat:
nctools.concat(frame, concat, name='variable', inplace=True)
if pivot:
frame = pd.pivot_table(frame, index=['reftime','leadtime','location'], columns='variable', values='value')
frame.reset_index(inplace=True)
if sort_by: frame.sort(sort_by, inplace=True)
if order_by:
frame = frame[order_by]
if out:
out = substitute.sub_date(out, init_time=prior)
if split_by:
gb = frame.groupby(split_by)
for key,group in gb:
if out:
new_name = _merge_name(out,key)
save(gb.get_group(key), new_name, config['format'], float_format=config.get('float-format'))
else:
print gb.get_group(key).to_string()
print '\n\n\n'
elif out:
save(frame, out, config['format'], float_format=config.get('float-format'))
else:
print frame.to_string()
def power(config):
"""Reads 'time series' from netcdf time series file, and adds power as a variable. """
if __name__ == "__main__":
logger = loghelper.create_logger(config)
else:
logger = loghelper.get_logger(config['log.name'])
# listify ensures they are returned as a list, even if it is one file
files = shared._listify(config['<files>'])
# Number of samples to use should be in here
# Whether to normalise power should be in here
start = config.get('start')
delay = config.get('delay')
cycles = shared._listify(config.get('cycles'))
pnorm = config.get('pnorm')
pdist = config.get('pdist')
sstd = config.get('sstd')
dstd = config.get('dstd')
pquants = config.get('pquants')
quantiles = np.array(pquants)
pcurve_dir = config.get('pcurve-dir')
ts_dir = config.get('tseries-dir')
out = config.get('out')
metadata = config.get('metadata')
basetime = start if start else datetime.datetime.today()
prior = shared._prior_time(basetime, delay=delay, hours=cycles)
logger.debug("using %s as a start time" % prior)
if not files:
logger.debug("no files specified, finding using options")
file_pattern = config.get('file-pattern')
if not file_pattern:
raise ConfigError('either supply files or specify file-pattern')
expanded = substitute.sub_date(file_pattern, init_time=prior)
files = glob.glob(expanded)
print("hello")
logger.debug(files)
# if we get to this point and there are still no files, then we have a problem
if not files: raise IOError("no files found")
logger.debug("input files: ")
logger.debug(files)
for f in files:
logger.debug("\t%s" % f)
# if pdist
if pdist: n = pdist
#grid_id = config['grid_id']
out_pattern = config.get('out')
for tseries_file in files:
dataset_in = Dataset(tseries_file, 'a')
# Get dimensions
dims = dataset_in.dimensions
nreftime = len(dims['reftime'])
ntime = len(dims['leadtime'])
nloc = len(dims['location'])
nheight = len(dims['height'])
loc_str_len = len(dims['loc_str_length'])
# Get coordinate variables
reftime = dataset_in.variables['reftime']
leadtime = dataset_in.variables['leadtime']
validtime = nctools._valid_time(reftime, leadtime)
refdt = num2date(reftime[:], reftime.units)
power_file = substitute.sub_date(out, init_time=refdt[0])
logger.info('Estimating power from time series: %s ' % tseries_file)
logger.info('Writing power time series to: %s ' % power_file)
location = [
''.join(l.filled(' ')).strip()
for l in dataset_in.variables['location']
]
height = dataset_in.variables['height']
if power_file == tseries_file:
dataset_out = dataset_in
else:
dataset_out = Dataset(power_file, 'w')
# Get number of quantiles
nq = len(quantiles)
pdata = np.ma.zeros((ntime, nloc, nheight, nq + 1),
np.float) # mean will be 1st value
use_locs = []
# loop through locations and look for power-curve file
for l, loc in enumerate(location):
pcurve_file = '%s/%s.csv' % (pcurve_dir, loc)
# mask power data if no power curve found for this park
if not os.path.exists(pcurve_file):
#logger.debug("Power curve: %s not found, skipping" % pcurve_file)
pdata[:, l, :, :] = np.ma.masked
continue
logger.info('Predicting power output for %s' % loc)
#
# Open power curve
#
use_locs.append(l)
pcurve = from_file(pcurve_file)
for h in range(nheight):
speed = dataset_in.variables['SPEED'][0, :, l, h]
direction = dataset_in.variables['DIRECTION'][0, :, l, h]
#pwr = pcurve.power(speed,direction)
# pdist will create a distribution for each timetep based on sampling
# n times from a normal distribution.
pdist = pcurve.power_dist(speed,
direction,
sstd=sstd,
dstd=dstd,
n=n,
normalise=pnorm)
pmean = np.mean(pdist, axis=1)
pquants = scipy.stats.mstats.mquantiles(pdist,
prob=quantiles / 100.0,
axis=1,
alphap=0.5,
betap=0.5)
pdata[:, l, h, 0] = pmean
pdata[:, l, h, 1:] = pquants[:, :]
#logger.info('finished %s' % loc)
use_inds = np.array(use_locs)
if dataset_out != dataset_in:
dataset_out.createDimension('reftime', None)
dataset_out.createVariable('reftime', 'float', ('reftime', ))
dataset_out.variables['reftime'][:] = reftime[:]
dataset_out.variables['reftime'].units = reftime.units
dataset_out.variables['reftime'].calendar = reftime.calendar
dataset_out.variables['reftime'].long_name = reftime.long_name
dataset_out.variables[
'reftime'].standard_name = reftime.standard_name
dataset_out.createDimension('leadtime', len(leadtime))
dataset_out.createVariable('leadtime', 'int', ('leadtime', ))
dataset_out.variables['leadtime'][:] = leadtime[:]
dataset_out.variables['leadtime'].units = leadtime.units
dataset_out.variables['leadtime'].long_name = leadtime.long_name
dataset_out.variables[
'leadtime'].standard_name = leadtime.standard_name
dataset_out.createDimension('location', len(use_locs))
dataset_out.createDimension('loc_str_length', loc_str_len)
loc_data = np.array(
[list(l.ljust(loc_str_len, ' ')) for l in location])
dataset_out.createVariable('location', 'c',
('location', 'loc_str_length'))
dataset_out.variables['location'][:] = loc_data[use_inds, :]
dataset_out.createDimension('height', nheight)
dataset_out.createVariable('height', 'i', ('height', ))
dataset_out.variables['height'][:] = height[:]
dataset_out.GRID_ID = dataset_in.GRID_ID
dataset_out.DX = dataset_in.DX
dataset_out.DY = dataset_in.DY
try:
dataset_out.variables['height'].units = height.units
except Exception:
logger.warn("height units missing")
pdata = pdata[:, use_inds, :, :]
for key in metadata.keys():
key = key.upper()
dataset_out.setncattr(key, dataset_in.getncattr(key))
pavg = dataset_out.createVariable(
'POWER', 'f', ('reftime', 'leadtime', 'location', 'height'))
pavg.units = 'kW'
pavg.description = 'forecast power output'
pavg[0, :, :, :] = pdata[:, :, :, 0]
for q, qval in enumerate(quantiles):
varname = 'POWER.P%02d' % qval
var = dataset_out.createVariable(
varname, 'f', ('reftime', 'leadtime', 'location', 'height'))
if pnorm:
var.units = 'ratio'
else:
var.units = 'kW'
var.description = 'forecast power output'
var[0, :, :, :] = pdata[:, :, :, q + 1]
#logger.debug(dataset_out)
dataset_in.close()
if dataset_out != dataset_in:
dataset_out.close()
