Subversion Repositories lagranto.arpege

#!/usr/bin/env python

# -*- coding:utf-8 -*-

import scipy

import netCDF4

import datetime

import magic

import copy

'''

TODO :

-Make write_netcdf work when usedatetime=False

'''

class Tra(object):

    """

    Class to work with LAGRANTO output

    Read trajectories from a LAGRANTO file and return a structured numpy array

    filename : file containing lagranto trajectories

    return :   structured numpy array traj(ntra,ntime) with variables as tuple.

    Example:

        trajs = Tra(filename)

        trajs['lon'][0,:] : return the longitudes for the first trajectory.

    Author : Nicolas Piaget, ETH Zurich , 2014

             Sebastiaan Crezee, ETH Zurich , 2014

    """

    read_format = {

        'NetCDF Data Format data': 'netcdf',

        'ASCII text': 'ascii',

    }

    def __init__(self, filename, typefile=None, usedatetime=True):

        """

        Read a file based on its type.

        By default typefile is determined automatically,

        but it can also be specified explicitly

        as a string argument:'netcdf' or 'ascii'

        """

        if typefile is None:

            typefile = magic.from_file(filename)

            error = "Unkown fileformat. Known formats are {}".format(

                    ":".join(self.read_format.keys())

            )

            typefile = self.read_format[typefile]

        else:

            error = "Unkown fileformat. Known formats are ascii or netcdf"

        try:

            function = '_read_{}'.format(typefile)

            self._array = globals()[function](filename,

                                              usedatetime=usedatetime)

        except IOError:

            raise IOError(error)

    def __len__(self):

        return len(self._array)

    def __getattr__(self, attr):

        if attr in self.__dict__:

            return getattr(self, attr)

        return getattr(self._array, attr)

    def __getitem__(self, key):

        return self._array[key]

    def __setitem__(self, key, item):

        if type(key) is slice:

            self._array = item

        else:

            formats = self._array.dtype.descr + [(key, item.dtype.descr[0][1])]

            formats = [(form[0].encode('ascii', 'ignore'),

                       form[1]) for form in formats]

            newarr = scipy.zeros(self._array.shape, dtype=formats)

            for var in self.variables:

                newarr[var] = self._array[var]

            newarr[key] = item

            self._array = newarr

    def __repr__(self):

        string = " \

        {} trajectories with {} time steps. \n \

        Available fields: {}\n \

        total duration: {} minutes".format(

            self.ntra, self.ntime,

            "/".join(self.variables),

            self.duration

        )

        return string

    @property

    def ntra(self):

        if self.ndim < 2:

            print(" \

                Be careful with the dimensions, \

                you may want to change the shape: \n \

                either shape + (1,) or (1,)+shape \

                ")

            return None

        return self.shape[0]

    @property

    def ntime(self):

        if self.ndim < 2:

            print(" \

                Be careful with the dimensions,\

                you may want to change the shape: \n \

                either shape + (1,) or (1,)+shape \

                ")

            return None

        return self.shape[1]

    @property

    def variables(self):

        return list(self.dtype.names)

    @property

    def duration(self):

        """ time duration in minutes """

        end = self['time'][0, -1]

        end = end.astype(datetime.datetime)

        start = self['time'][0, 0]

        start = start.astype(datetime.datetime)

        delta = end - start

        return delta.total_seconds() / 60.

    @property

    def initial(self):

        """ give the initial time of the trajectories """

        starttime = self['time'][0, 0]

        return starttime

    def set_array(self, array):

        """ To change the trajectories array """

        self._array = array

    def concatenate(self, trajs):

        """ To concatenate trajectories together

            return Tra object

        """

        if type(trajs) is not tuple:

            trajs = (trajs,)

        trajstuple = tuple(tra._array for tra in trajs)

        trajstuple += (self._array,)

        newtrajs = copy.copy(self)

        test = scipy.concatenate(trajstuple)

        newtrajs._array = test

        return newtrajs

    def write(self, filename, fileformat='netcdf'):

        globals()['_write_{}'.format(fileformat)](self, filename)

# -----------------------------------------------------------------------------

# read a netcdf lsl file

# -----------------------------------------------------------------------------

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:

            vardata = ncfile.variables[variable][:, 0, 0, :][:].T

        else:

            vardata = ncfile.variables[variable][:].T

        vardata[vardata < -990] = scipy.nan

        array[nvariable] = vardata

    # 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

# ------------------------------------------------------------------------------

# read an ASCII lsl file

# -----------------------------------------------------------------------------

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

# ------------------------------------------------------------------------------

# write trajectories to a netcdf file

# ------------------------------------------------------------------------------

def _write_netcdf(Tra, filename):

    ncfile = netCDF4.Dataset(filename, 'w', format='NETCDF3_CLASSIC')

    ncfile.createDimension('ntra', Tra.ntra)

    ncfile.createDimension('ntim', Tra.ntime)

    ncfile.ref_year, ncfile.ref_month, ncfile.ref_day, ncfile.ref_hour,\

        ncfile.ref_min = Tra.initial.astype(datetime.datetime).timetuple()[0:5]

    vararray = ncfile.createVariable('time', 'f4', ('ntim', ))

    delta = Tra['time'][0, :] - Tra.initial

    time = [int(a.astype(datetime.datetime).total_seconds() / 3600) +

            (a.astype(datetime.datetime).total_seconds() -

             int(a.astype(datetime.datetime).total_seconds() / 3600) * 3600)

            / 60 * 0.01 for a in delta]

    vararray[:] = time

    # add variables

    for var in Tra.variables:

        if var == 'time':

            continue

        vararray = ncfile.createVariable(var, Tra[var].dtype, ('ntim', 'ntra'))

        vararray[:] = Tra[var].T

    ncfile.close()