def _read_ascii(filename, usedatetime, nhead=5):
""" Read a lsl file from ASCII """
# open the file
open_file = open(filename, 'r')
# get the header
file_lines = open_file.readlines()
nvariables = file_lines[2].strip().split()
head = file_lines[0].split()
# read starting time new and old formats
try:
starttime = datetime.datetime.strptime(head[2], '%Y%m%d_%H%M')
except ValueError:
try:
starttime = datetime.datetime.strptime(head[2] + '_' + head[3],
'%Y%m%d_%H')
except:
print("Warning: could not retrieve starttime from header,\
setting to default value ")
starttime = datetime.datetime(1970, 1, 1)
if usedatetime:
dtypes = [
'f8' if var != 'time' else datetime.datetime for var in nvariables
]
else:
dtypes = ['f8' for var in nvariables]
# read the content as numpy array
array = scipy.genfromtxt(filename,
dtype=dtypes,
names=nvariables,
skip_header=nhead,
missing_values=-999.99)
# find characteristic of trajectories
timestep = float(array[1][0]) - float(array[0][0])
period = float(array[-1][0]) - float(array[0][0])
# Convert minutes to decimal hours
if max((array['time'].astype(float)) % 1) <= 0.60:
timestep = scipy.floor(timestep) + ((timestep % 1) / .60)
period = scipy.floor(period) + ((period % 1) / .60)
# period/timestep gives strange offset (related to precision??)
# so use scipy.around
ntime = int(1 + scipy.around(period / timestep))
ntra = int(array.size / ntime)
# reshape traj file
array = scipy.reshape(array, (ntra, ntime))
if usedatetime:
# change time into datetime object
time = scipy.array([
scipy.datetime64(starttime + datetime.timedelta(hours=t))
for t in scipy.arange(0, period + timestep, timestep)
])
time.shape = (1, ) + time.shape
time = time.repeat(array.shape[0], axis=0)
array['time'] = 0
newdtypes = [
descr if descr[0] != 'time' else ('time', 'datetime64[s]')
for descr in array.dtype.descr
]
array = array.astype(newdtypes)
array['time'] = time
return array
def _read_ascii(filename, usedatetime, nhead=5):
""" Read a lsl file from ASCII """
# open the file
open_file = open(filename, 'r')
# get the header
file_lines = open_file.readlines()
nvariables = file_lines[2].strip().split()
head = file_lines[0].split()
# read starting time new and old formats
try:
starttime = datetime.datetime.strptime(head[2], '%Y%m%d_%H%M')
except ValueError:
try:
starttime = datetime.datetime.strptime(
head[2] + '_' + head[3], '%Y%m%d_%H'
)
except:
print("Warning: could not retrieve starttime from header,\
setting to default value ")
starttime = datetime.datetime(1970, 1, 1)
if usedatetime:
dtypes = ['f8' if var != 'time' else datetime.datetime
for var in nvariables]
else:
dtypes = ['f8' for var in nvariables]
# read the content as numpy array
array = scipy.genfromtxt(filename,
dtype=dtypes,
names=nvariables,
skip_header=nhead,
missing_values=-999.99)
# find characteristic of trajectories
timestep = float(array[1][0]) - float(array[0][0])
period = float(array[-1][0]) - float(array[0][0])
# Convert minutes to decimal hours
if max((array['time'].astype(float)) % 1) <= 0.60:
timestep = scipy.floor(timestep) + ((timestep % 1) / .60)
period = scipy.floor(period) + ((period % 1) / .60)
# period/timestep gives strange offset (related to precision??)
# so use scipy.around
ntime = int(1 + scipy.around(period / timestep))
ntra = int(array.size / ntime)
# reshape traj file
array = scipy.reshape(array, (ntra, ntime))
if usedatetime:
# change time into datetime object
time = scipy.array(
[scipy.datetime64(starttime + datetime.timedelta(hours=t))
for t in scipy.arange(0, period + timestep, timestep)]
)
time.shape = (1,) + time.shape
time = time.repeat(array.shape[0], axis=0)
array['time'] = 0
newdtypes = [descr if descr[0] != 'time' else ('time', 'datetime64[s]')
for descr in array.dtype.descr]
array = array.astype(newdtypes)
array['time'] = time
return array
def _read_netcdf(filename, usedatetime):
""" Read a netcdf lsl file """
# read the netcdf, the formats and the variables names
ncfile = netCDF4.Dataset(filename)
if usedatetime:
formats = [
var[1].dtype if var[0] != 'time' else 'datetime64[s]'
for var in list(ncfile.variables.items())
]
else:
formats = [var[1].dtype for var in list(ncfile.variables.items())]
variables = list(ncfile.variables.keys())
# set the numbers of trajectories and time step
try:
ntra = len(ncfile.dimensions['dimx_lon'])
except:
try:
ntra = len(ncfile.dimensions['id'])
except:
try:
ntra = len(ncfile.dimensions['ntra'])
except:
raise Exception('Cannot read the number of trajectories,\
not one of dimx_lon, id or ntra')
try:
ntime = len(ncfile.dimensions['time'])
except:
ntime = len(ncfile.dimensions['ntim'])
# change names of the coordinates if necessary
nvariables = list(variables)
if "longitude" in variables:
nvariables[variables.index("longitude")] = "lon"
if "latitude" in variables:
nvariables[variables.index("latitude")] = "lat"
# create a structured array using numpy
array = scipy.zeros((ntra, ntime),
dtype={
'names': nvariables,
'formats': formats
})
for variable in variables:
if variable == 'BASEDATE':
continue
nvariable = variable
if variable == 'longitude':
nvariable = 'lon'
if variable == 'latitude':
nvariable = 'lat'
if len(ncfile.variables[variable].shape) == 4:
array[nvariable] = ncfile.variables[variable][:, 0, 0, :][:].T
else:
array[nvariable] = ncfile.variables[variable][:].T
# read the starting date and duration
try:
date = [int(i) for i in ncfile.variables['BASEDATE'][0, 0, 0, :]]
starttime = datetime.datetime(date[0], date[1], date[2], date[3],
date[4])
except:
starttime = datetime.datetime(ncfile.ref_year, ncfile.ref_month,
ncfile.ref_day, ncfile.ref_hour,
ncfile.ref_min)
# find characteristic of trajectories
timestep = ncfile.variables['time'][1] - ncfile.variables['time'][0]
period = ncfile.variables['time'][-1] - ncfile.variables['time'][0]
# True if time = hh.mm
cond1 = 0.00 < (ncfile.variables['time'][:] % 1).max() <= 0.60
if cond1:
timestep = scipy.floor(timestep) + ((timestep % 1) / .60)
timestep = scipy.around(timestep, 6)
period = scipy.floor(period) + ((period % 1) / .60)
period = scipy.around(period, 6)
# transform the times into datetime object
# special treatment for online trajectories (time given in minutes)
if usedatetime:
try:
time = scipy.array([
scipy.datetime64(starttime + datetime.timedelta(hours=t))
for t in scipy.arange(0, period + timestep, timestep)
])
except AttributeError:
time = scipy.array([
scipy.datetime64(starttime + datetime.timedelta(seconds=t))
for t in scipy.arange(0, period + timestep, timestep)
])
time.shape = (1, ) + time.shape
time = time.repeat(array.shape[0], axis=0)
array['time'] = time
ncfile.close()
return array
def _read_netcdf(filename, usedatetime):
""" Read a netcdf lsl file """
# read the netcdf, the formats and the variables names
ncfile = netCDF4.Dataset(filename)
if usedatetime:
formats = [var[1].dtype if var[0] != 'time' else 'datetime64[s]'
for var in list(ncfile.variables.items())]
else:
formats = [var[1].dtype for var in list(ncfile.variables.items())]
variables = list(ncfile.variables.keys())
# set the numbers of trajectories and time step
try:
ntra = len(ncfile.dimensions['dimx_lon'])
except:
try:
ntra = len(ncfile.dimensions['id'])
except:
try:
ntra = len(ncfile.dimensions['ntra'])
except:
raise Exception('Cannot read the number of trajectories,\
not one of dimx_lon, id or ntra')
try:
ntime = len(ncfile.dimensions['time'])
except:
ntime = len(ncfile.dimensions['ntim'])
# change names of the coordinates if necessary
nvariables = list(variables)
if "longitude" in variables:
nvariables[variables.index("longitude")] = "lon"
if "latitude" in variables:
nvariables[variables.index("latitude")] = "lat"
# create a structured array using numpy
array = scipy.zeros((ntra, ntime),
dtype={'names': nvariables, 'formats': formats})
for variable in variables:
if variable == 'BASEDATE':
continue
nvariable = variable
if variable == 'longitude':
nvariable = 'lon'
if variable == 'latitude':
nvariable = 'lat'
if len(ncfile.variables[variable].shape) == 4:
array[nvariable] = ncfile.variables[variable][:, 0, 0, :][:].T
else:
array[nvariable] = ncfile.variables[variable][:].T
# read the starting date and duration
try:
date = [int(i) for i in ncfile.variables['BASEDATE'][0, 0, 0, :]]
starttime = datetime.datetime(date[0], date[1],
date[2], date[3], date[4])
except:
starttime = datetime.datetime(ncfile.ref_year,
ncfile.ref_month,
ncfile.ref_day,
ncfile.ref_hour,
ncfile.ref_min)
# find characteristic of trajectories
timestep = ncfile.variables['time'][1] - ncfile.variables['time'][0]
period = ncfile.variables['time'][-1] - ncfile.variables['time'][0]
# True if time = hh.mm
cond1 = 0.00 < (ncfile.variables['time'][:] % 1).max() <= 0.60
if cond1:
timestep = scipy.floor(timestep) + ((timestep % 1) / .60)
timestep = scipy.around(timestep, 6)
period = scipy.floor(period) + ((period % 1) / .60)
period = scipy.around(period, 6)
# transform the times into datetime object
# special treatment for online trajectories (time given in minutes)
if usedatetime:
try:
time = scipy.array(
[scipy.datetime64(starttime + datetime.timedelta(hours=t))
for t in scipy.arange(0, period + timestep, timestep)]
)
except AttributeError:
time = scipy.array(
[scipy.datetime64(starttime + datetime.timedelta(seconds=t))
for t in scipy.arange(0, period + timestep, timestep)]
)
time.shape = (1,) + time.shape
time = time.repeat(array.shape[0], axis=0)
array['time'] = time
ncfile.close()
return array
