def array_to_loc_list(station_array):
"""
Convert a :class:`numpy.ndarray` into a list of
:class:`~polyadcirc.pyADCIRC.basic.location`.
:param station_array: an array of shape (n, 2) where n is the number of
station locations
:type station_array: :class:`numpy.ndarray`
:rtype: list
:returns: list of :class:`~polyadcirc.pyADCIRC.basic.location`
"""
stations = []
for i in station_array:
stations.append(basic.location(i[0], i[1]))
return stations
def _read_record7(fid, key1, key2, line, data):
"""
Saves metadata to ``data.stations[key]`` and ``data.recording[key]`` that
is used to preallocate arrays for data recording for ``fort.7#`` type
ADCIRC output files
:param fid: :class:``file`` object
:param string key1: ADCIRC Output File Type sans ``.``
:param string key2: ADCIRC Output File Type sans ``.``
:param line: array of parameters read from ``fort.15`` file
:type line: :class:``numpy.ndarray``
:param data: object to store mesh specific data
:type data: :class:``~polyadcirc.run_framework.domain``
:rtype: string
:returns: station type description
"""
nout, touts, toutf, nspool = np.fromstring(line[0].strip(), sep=' ')
touts = max(touts, data.time.statim)
toutf = min(toutf, data.time.rnday+data.time.statim)
description = None
if nout != 0 and nspool != 0:
total_obs = int((toutf - touts) * 24.0 * 60 * 60 / data.time.dt/ nspool)
else:
total_obs = 0
if filetype[key1][0]:
line = fid.readline().partition('!')
meas_locs = int(line[0].strip())
stations = []
description = line[-1]
for i in xrange(meas_locs):
line = fid.readline()
line = line.partition('!')
line = re.findall(r"[-*\d\.\d]+", line[0].strip())
stations.append(basic.location(float(line[0]),
float(line[-1])))
data.stations[key1] = stations
data.stations[key2] = stations
else:
meas_locs = data.node_num
data.recording[key1] = (meas_locs, total_obs, filetype[key1][1])
data.recording[key2] = (meas_locs, total_obs, filetype[key2][1])
return description
wall_points = np.column_stack((xmin, xmax, ymin, ymax, wall_height))
wall_points = wall_points.transpose()
# setup and save to shelf
# set up saving
save_file = 'py_save_file'
stat_x = np.concatenate((1900*np.ones((7,)), [1200], 1300*np.ones((3,)),
[1500]))
stat_y = np.array([1200, 600, 300, 0, -300, -600, -1200, 0, 1200, 0, -1200,
-1400])
stations = []
for x, y in zip(stat_y, stat_y):
stations.append(basic.location(x, y))
# Run experiments
# MainFile_RandomMann
main_run.run_nobatch_q(domain, wall_points, mann_pts, save_file,
num_procs=nprocs, procs_pnode=ppnode, stations=stations, TpN=TpN)
xmin = 1420
xmax = 1580
ymax = 1500
wall_height = -2.5
param_min = lam_domain[:, 0]
param_max = lam_domain[:, 1]
# Create stations
stat_x = np.concatenate((1900*np.ones((7,)), [1200], 1300*np.ones((3,)),
[1500]))
stat_y = np.array([1200, 600, 300, 0, -300, -600, -1200, 0, 1200,
0, -1200, -1400])
all_stations = []
for x, y in zip(stat_x, stat_y):
all_stations.append(basic.location(x, y))
# Select only the stations I care about this will lead to better sampling
station_nums = [0, 5] # 1, 6
stations = []
for s in station_nums:
stations.append(all_stations[s])
# Create Transition Kernel
transition_set = asam.transition_set(.5, .5**5, 1.0)
# Read in Q_ref and Q to create the appropriate rho_D
mdat = sio.loadmat('Q_2D')
Q = mdat['Q']
Q = Q[:, station_nums]
Q_ref = mdat['Q_true']
