Python NetCDFFile.variables['time'][:]方法代码示例

# 需要导入模块: from anuga.file.netcdf import NetCDFFile [as 别名]
# 或者: from anuga.file.netcdf.NetCDFFile import variables['time'][:] [as 别名]
def prepare_timeboundary(filename, verbose = False):
    """Convert benchmark 2 time series to NetCDF tms file.
    This is a 'throw-away' code taylor made for files like
    'Benchmark_2_input.txt' from the LWRU2 benchmark
    """

    from anuga.file.netcdf import NetCDFFile

    if verbose: print 'Creating', filename

    # Read the ascii (.txt) version of this file
    fid = open(filename[:-4] + '.txt')

    # Skip first line
    line = fid.readline()

    # Read remaining lines
    lines = fid.readlines()
    fid.close()


    N = len(lines)
    T = num.zeros(N, num.float)  #Time
    Q = num.zeros(N, num.float)  #Values

    for i, line in enumerate(lines):
        fields = line.split()

        T[i] = float(fields[0])
        Q[i] = float(fields[1])


    # Create tms NetCDF file

    fid = NetCDFFile(filename, 'w')
    fid.institution = 'Geoscience Australia'
    fid.description = 'Input wave for Benchmark 2'
    fid.starttime = 0.0
    fid.createDimension('number_of_timesteps', len(T))
    fid.createVariable('time', netcdf_float, ('number_of_timesteps',))
    fid.variables['time'][:] = T

    fid.createVariable('stage', netcdf_float, ('number_of_timesteps',))
    fid.variables['stage'][:] = Q[:]

    fid.createVariable('xmomentum', netcdf_float, ('number_of_timesteps',))
    fid.variables['xmomentum'][:] = 0.0

    fid.createVariable('ymomentum', netcdf_float, ('number_of_timesteps',))
    fid.variables['ymomentum'][:] = 0.0

    fid.close()

# 需要导入模块: from anuga.file.netcdf import NetCDFFile [as 别名]
# 或者: from anuga.file.netcdf.NetCDFFile import variables['time'][:] [as 别名]

#.........这里部分代码省略.........
    if file_text[-4:] != '.txt':
        raise IOError('Input file %s should be of type .txt.' % file_text)

    if file_out is None:
        file_out = file_text[:-4] + '.tms'

    fid = open(file_text)
    line = fid.readline()
    fid.close()

    fields = line.split(',')
    msg = "File %s must have the format 'datetime, value0 value1 value2 ...'" \
          % file_text
    assert len(fields) == 2, msg

    if not time_as_seconds:
        try:
            starttime = calendar.timegm(time.strptime(fields[0], time_format))
        except ValueError:
            msg = 'First field in file %s must be' % file_text
            msg += ' date-time with format %s.\n' % time_format
            msg += 'I got %s instead.' % fields[0]
            raise DataTimeError, msg
    else:
        try:
            starttime = float(fields[0])
        except Error:
            msg = "Bad time format"
            raise DataTimeError, msg

    # Split values
    values = []
    for value in fields[1].split():
        values.append(float(value))

    q = ensure_numeric(values)

    msg = 'ERROR: File must contain at least one independent value'
    assert len(q.shape) == 1, msg

    # Read times proper
    from anuga.config import time_format
    import time, calendar

    fid = open(file_text)
    lines = fid.readlines()
    fid.close()

    N = len(lines)
    d = len(q)

    T = num.zeros(N, num.float)       # Time
    Q = num.zeros((N, d), num.float)  # Values

    for i, line in enumerate(lines):
        fields = line.split(',')
        if not time_as_seconds:
            realtime = calendar.timegm(time.strptime(fields[0], time_format))
        else:
            realtime = float(fields[0])
        T[i] = realtime - starttime

        for j, value in enumerate(fields[1].split()):
            Q[i, j] = float(value)

    msg = 'File %s must list time as a monotonuosly ' % file_text
    msg += 'increasing sequence'
    assert num.alltrue(T[1:] - T[:-1] > 0), msg

    #Create NetCDF file
    fid = NetCDFFile(file_out, netcdf_mode_w)

    fid.institution = 'Geoscience Australia'
    fid.description = 'Time series'

    #Reference point
    #Start time in seconds since the epoch (midnight 1/1/1970)
    #FIXME: Use Georef
    fid.starttime = starttime

    # dimension definitions
    #fid.createDimension('number_of_volumes', self.number_of_volumes)
    #fid.createDimension('number_of_vertices', 3)

    fid.createDimension('number_of_timesteps', len(T))

    fid.createVariable('time', netcdf_float, ('number_of_timesteps',))

    fid.variables['time'][:] = T

    for i in range(Q.shape[1]):
        try:
            name = quantity_names[i]
        except:
            name = 'Attribute%d' % i

        fid.createVariable(name, netcdf_float, ('number_of_timesteps',))
        fid.variables[name][:] = Q[:,i]

    fid.close()