# Source code for spacepy.pybats

    # -*- coding: utf-8 -*-
    '''
    PyBats!  An open source Python-based interface for reading, manipulating,
    and visualizing BATS-R-US and SWMF output.
    For more information on the SWMF, please visit the
    `Center for Space Environment Modeling <http://csem.engin.umich.edu>`_.

    Introduction
    ------------

    At its most fundamental level, PyBats provides access to output files written
    by the Space Weather Modeling Framework and the codes contained within.
    The key task performed by PyBats is loading simulation data into a Spacepy
    data model object so that the user can move on to the important tasks of
    analyzing and visualizing the values.  The secondary goal of PyBats is to make
    common tasks performed with these data as easy as possible.  The result is that
    most SWMF output can be opened and visualized using only a few lines of code.
    Many complicated tasks, such as field line integration, is included as well.

    Organization
    ------------

    Many output files in the SWMF share a common format.  Objects to handle broad
    formats like these are found in the base module.  The base module has classes
    to handle SWMF *input* files, as well.

    The rest of the classes are organized by code, i.e. classes and functions
    specifically relevant to BATS-R-US can be found in
    :mod:`spacepy.pybats.bats`.  Whenever a certain code included in the SWMF
    requires a independent class or a subclass from the PyBats base module, it
    will receive its own submodule.

    Conventions and Prefixes
    ------------------------

    Nearly every class in PyBats inherits from :class:`spacepy.datamodel.SpaceData`,
    so it is important for users to understand how to employ and explore SpaceData
    objects.  There are a few exceptions, so always pay close attention to the
    docstrings and examples.  Legacy code that does not adhere to this pattern is
    slowly being brought up-to-date with each release.

    Visualization methods have two prefixes: *plot_* and *add_*.  Whenever a method
    begins with *plot_*, a quick-look product will be created that is not highly-
    configurable.  These methods are meant to yeild either simple
    diagnostic plots or static, often-used products.  There are few methods that use
    this prefix.  The prefix *add_* is always followed by *plot_type*; it indicates
    a plotting method that is highly configurable and meant to be combined with
    other *add_*-like methods and matplotlib commands.

    Common calculations, such as calculating Alfven wave speeds of MHD results,
    are strewn about PyBats' classes.  They are always given the method prefix
    *calc_*, i.e. *calc_alfven*.  Methods called *calc_all* will search for all
    class methods with the *calc_* prefix and call them.

    Copyright ©2010 Los Alamos National Security, LLC.

    Submodules
    ----------

    There are submodules for most models included within the SWMF.  The classes
    and methods contained within are code-specific, yielding power and
    convenience at the cost of flexibility.  A few of the submodules are helper
    modules- they are not code specific, but rather provide functionality not
    related to an SWMF-included code.

    .. autosummary::
       :template: clean_module.rst
       :toctree: autosummary

       bats
       dgcpm
       dipole
       gitm
       kyoto
       pwom
       ram
       rim
       trace2d

    Top-Level Classes & Functions
    -----------------------------

    Top-level PyBats classes handle common-format input and output from the SWMF
    and are very flexible.  However, they do little beyond open files for the user.

    There are several functions found in the top-level module.  These are mostly
    convenience functions for customizing plots.

    .. rubric:: Classes
    .. autosummary::
       :template: clean_class.rst
       :toctree: autosummary

       IdlFile
       ImfInput
       LogFile
       NgdcIndex
       PbData
       SatOrbit

    .. rubric:: Functions
    .. autosummary::
       :template: clean_function.rst
       :toctree: autosummary

       add_body
       add_planet
       parse_tecvars

    '''

    __contact__ = 'Dan Welling, daniel.welling@uta.edu'

    # Global imports (used ubiquitously throughout this module.
    import os.path
    from functools import wraps

    from spacepy.datamodel import dmarray, SpaceData
    import numpy as np

    # Pybats-related decorators:
    def calc_wrapper(meth):
        '''
        This is a decorator for `self.calc_*` object methods.  It establishes
        `self._calcs`, a list of calcuation functions that have been called, and
        adds the called function to that list to keep track of which calculations
        have already been performed for the object.  If a calculation has already
        been performed, it is skipped in order to reduce effort.
        '''

        @wraps(meth)
        def wrapped(self, *args, **kwargs):
            # Establish list of calculations:
            if not hasattr(self, '_calcs'): self._calcs={}

            # Check to see if we're in the list, return if true.
            if meth.__name__ in self._calcs: return

            # If not, add to list and stash args/kwargs:
            self._calcs[meth.__name__] = [args, kwargs]

            # call method:
            return meth(self, *args, **kwargs)

        # Return decorated function:
        return wrapped

    # Some common, global functions.
    def parse_filename_time(filename):
        '''
        Given an SWMF file whose name follows the usual standards (see below),
        attempt to parse the file name to extract information about the iteration,
        runtime and date/time at which the file was written.  All three are
        returned to the caller in that order.  If any cannot be found in the
        file name, "None" is returned in its place.

        For *.outs files, ranges of times and iterations can be given in the
        file names.  If this is the case, a list of values will be returned
        for that entry (see third example below).

        Information on the format of time strings contained within SWMF output
        file names can be found in the `SWMF User Manual
        <http://herot.engin.umich.edu/~gtoth/SWMF/doc/HTML/SWMF/node225.html>`_.

        Parameters
        ==========
        filename : string
            An SWMF output file name.

        Other Parameters
        ================
        None

        Returns
        =======
        i_iter : integer, list of integers, or None
            The iteration at which the file was written, if found.
        runtime : float, list of floats, or None
            The run time (in seconds) at which the file was written, if found.
        time : datetime.datetime, list of datetimes, or None
            Either a datetime object or *None*, depending on the success of the
            file name parsing.

        Examples
        ========
        >>> from spacepy.pybats import parse_filename_time
        >>> parse_filename_time('z=0_mhd_2_t00050000_n00249620.out')
        (249620, 50000.0, None)

        >>> parse_filename_time('mag_grid_e20130924-232600.out')
        (None, None, datetime.datetime(2013, 9, 24, 23, 26))

        >>> parse_filename_time('z=0_mhd_2_e20140410-000000-000_20140410-000300-000.outs')
        (None, None, [datetime.datetime(2014, 4, 10, 0, 0), datetime.datetime(2014, 4, 10, 0, 3)])
        '''

        from dateutil.parser import parse
        import re

        filename = os.path.basename(filename)

        # Look for date/time:
        if '_e' in filename:
            subname = re.search('_e((\d{8}\-\d{6}(\-\d{3})?\_?)+)', filename).groups()[0]
            t_string = re.findall('(\d{8}\-\d{6})', subname)
            time = [parse(x) for x in t_string]
            if len(time) == 1: time = time[0] # Reduce to scalar if necessary.
        else:
            time = None

        # Look for run time (can be time from start OR datetime):
        if '_t' in filename:
            subname = re.search('_t((\d+\_?)+)', filename).groups()[0]
            # Need to check if we're "old style" timestamps which is time (s) from
            # start of simultion or new style which are date/time strings.  Look
            # at number of digits to tell difference.
            groups = re.findall('\d+', subname)
            if len(groups[0]) == 14:
                time = [parse(x) for x in groups]
                if len(time) == 1: time = time[0]
                runtime = None
            else:
                runtime = [3600*float(x[:-4])+60*float(x[-4:-2])+float(x[-2:])
                           for x in groups]
                if len(runtime) == 1: runtime = runtime[0]
        else:
            runtime = None

        # Look for file iteration:
        if '_n' in filename:
            subname = re.search('_n((\d+\_?)+)', filename).groups()[0]
            i_iter = [int(x) for x in re.findall('\d+', subname)]
            if len(i_iter) == 1: i_iter = i_iter[0]  # Reduce to scalar if necessary.
        else:
            i_iter = None

        return i_iter, runtime, time

    def mhdname_to_tex(varname):
        '''
        Convert common MHD variable and unit names into LaTeX-formated strings.
        '''

        import re

        match_u = re.search('(.*)u([xyz])', varname)
        match_b = re.match('([bj])([xyz])', varname)

        if 'rho' in varname.lower():
            out = r'$\rho_{'+varname.replace('rho', '')+'}$'
        elif varname.lower() == 'nt':
            out = '$nT$'
        elif varname[:2] == 'dB':
            subscript = varname[2]+', '*(len(varname)>3)+varname[3:]
            out = r'$\Delta B _{'+subscript+'}$'
        elif varname.lower()[-1] == 'p':
            out = '$P_{'+varname[:-1]+'}$'
        elif match_u:
            out = '$U_{'+match_u.group(2)+', '*bool(match_u.group(1)) \
                  + match_u.group(1)+'}$'
        elif match_b:
            out = '$'+match_b.group(1).upper()+'_{'+match_b.group(2)+'}$'
        elif 'j' == varname[0].lower():
            out = '$J_{'+varname[1:]+'}$'
        else:
            out = varname

        return out

    def parse_tecvars(line):
        '''
        Parse the VARIABLES line from a TecPlot-formatted ascii data file.  Create
        a list of name-unit tuples for each variable.
        '''

        import re

        # Create return list.
        ret = []

        # Ensure that the input line is a TecPlot variable line.
        if "VARIABLES" not in line:
            raise ValueError('Input line is not a TecPlot VARIABLES line.')

        # Strip out "VARIABLES = "
        line=re.sub('(^\s*VARIABLES\s*\=\s*)|(")', '', line)

        # break into individual vars using commas.
        for s in line.split(','):
            m=re.match('\s*(\w+)\s*(\[(.*)\])*\s*', s)
            ret.append( (m.group(1).lower(), m.group(3)) )

        return ret

    def add_planet(ax, rad=1.0, ang=0.0, add_night=True, zorder=1000,
                   **extra_kwargs):
        '''
        Creates a circle of ``radius=self.para['rbody']`` and returns the
        MatPlotLib Ellipse patch object for plotting.  If an axis is specified
        using the *ax* keyword, the patch is added to the plot.

        Unlike the add_body method, the circle is colored half white (dayside)
        and half black (nightside) to coincide with the direction of the
        sun. Additionally, because the size of the planet is not intrinsically
        known to the MHD file, the kwarg "rad", defaulting to 1.0, sets the
        size of the planet.  *add_night* can turn off this behavior.

        Extra keywords are handed to the Ellipse generator function.

        Parameters
        ==========
        ax : Matplotlib Axes object
           Set the axes on which to place planet.

        Other Parameters
        ================
        rad : float
           Set radius of planet.  Defaults to 1.
        ang : float
           Set the rotation of the day-night terminator from the y-axis, in degrees.
           Defaults to zero (terminator is aligned with Y-axis.)
        add_night : boolean
           Add night hemisphere.  Defaults to **True**
        zorder : int
           Set the matplotlib zorder of the patch to set how other plot
           elements order with the inner boundary patch. Defaults to 1000,
           nightside patch is given zorder of *zorder+5*.

        '''

        from matplotlib.patches import Circle, Wedge

        body = Circle((0,0), rad, fc='w', zorder=zorder, **extra_kwargs)
        arch = Wedge((0,0), rad, 90+ang, -90+ang, fc='k',
                     zorder=zorder+5, **extra_kwargs)

        ax.add_artist(body)
        if add_night: ax.add_artist(arch)

        return body, arch

    def add_body(ax, rad=2.5, facecolor='lightgrey', show_planet=True,
                 ang=0.0, add_night=True, zorder=1000, **extra_kwargs):
        '''
        Creates a circle of radius=self.attrs['rbody'] and returns the
        MatPlotLib Ellipse patch object for plotting.  If an axis is specified
        using the "ax" keyword, the patch is added to the plot.
        Default color is light grey; extra keywords are handed to the Ellipse
        generator function.

        Because the body is rarely the size of the planet at the center of
        the modeling domain, add_planet is automatically called.  This can
        be negated by using the show_planet kwarg.

        Parameters
        ==========
        ax : Matplotlib Axes object
           Set the axes on which to place planet.

        Other Parameters
        ================
        rad : float
           Set radius of the inner boundary.  Defaults to 2.5.
        facecolor : string
           Set color of face of inner boundary circle via Matplotlib color
           selectors (name, hex, etc.)  Defaults to 'lightgrey'.
        show_planet : boolean
           Turns on/off planet indicator inside inner boundary.
           Defaults to **True**
        ang : float
           Set the rotation of the day-night terminator from the y-axis, in degrees.
           Defaults to zero (terminator is aligned with Y-axis.)
        add_night : boolean
           Add night hemisphere.  Defaults to **True**
        zorder : int
           Set the matplotlib zorder of the patch to set how other plot
           elements order with the inner boundary patch. Defaults to 1000.
           If a planet is added, it is given a zorder of *zorder*+5.

        '''
        from matplotlib.patches import Ellipse

        dbody = 2.0 * rad
        body = Ellipse((0,0),dbody,dbody,facecolor=facecolor, zorder=zorder,
                       **extra_kwargs)

        if show_planet:
            add_planet(ax, ang=ang, add_night=add_night, zorder=zorder+5)

        ax.add_artist(body)

    def _read_idl_ascii(pbdat, header='units', start_loc=0, keep_case=True):
        '''
        Load a SWMF IDL ascii output file and load into a pre-existing PbData
        object.  This should only be called by :class:`IdlFile`.

        The input object must have the name of the file to be opened and read
        stored in its attributes list and named 'file'.

        The kwarg *header* dictates how the header line will be handled.  In some
        output files, the header is simply the list of units.  In others, it
        contains other data.  If *header* is set to 'units', the header is
        assumed to be a list of units for each variable contained within the
        file.  If set to **None** or not recognized, the header will be saved
        in the object's attribute list under 'header'.

        Parameters
        ----------
        pbdat : PbData object
            The object into which the data will be loaded.

        Other Parameters
        ----------------
        header : string or **None**
            A string indicating how the header line will be handled; see above.
        start_loc : int
            Starting location within file of data to read in number of characters.
            Used when reading *.outs files with more than one data frame.
        keep_case : boolean
            If set to True, the case of variable names will be preserved.  If
            set to False, variable names will be set to all lower case.

        Returns
        -------
        True : Boolean
            Returns True on success.

        '''

        # Open the file:
        infile = open(pbdat.attrs['file'], 'r')

        # Read the top header line:
        headline = infile.readline().strip()

        # Read & convert iters, runtime, etc. from next line:
        parts = infile.readline().split()
        pbdat.attrs['iter']   = int(parts[0])
        pbdat.attrs['runtime']= float(parts[1])
        pbdat.attrs['ndim']   = int(parts[2])
        pbdat.attrs['nparam'] = int(parts[3])
        pbdat.attrs['nvar']   = int(parts[4])

        # Read & convert grid dimensions.
        grid = [int(x) for x in infile.readline().split()]
        pbdat['grid'] = dmarray(grid)

        # Data from generalized (structured but irregular) grids can be
        # detected by a negative ndim value.  Unstructured grids (e.g.
        # BATS, AMRVAC) are signified by negative ndim values AND
        # the grid size is always [x, 1(, 1)]
        # Here, we set the grid type attribute to either Regular,
        # Generalized, or Unstructured.  Let's set that here.
        pbdat['grid'].attrs['gtype'] = 'Regular'
        pbdat['grid'].attrs['npoints']  = abs(pbdat['grid'].prod())
        if pbdat.attrs['ndim'] < 0:
            if any(pbdat['grid'][1:] > 1):
                pbdat['grid'].attrs['gtype'] = 'Generalized'
            else:
                pbdat['grid'].attrs['gtype'] = 'Unstructured'
                pbdat['grid'].attrs['npoints'] = pbdat['grid'][0]
        pbdat.attrs['ndim'] = abs(pbdat.attrs['ndim'])

        # Quick ref vars:
        time=pbdat.attrs['runtime']
        gtyp=pbdat['grid'].attrs['gtype']
        npts=pbdat['grid'].attrs['npoints']
        ndim=pbdat['grid'].size
        nvar=pbdat.attrs['nvar']
        npar=pbdat.attrs['nparam']

        # Read parameters stored in file.
        para = np.zeros(npar)
        if npar>0:
            para[:] = infile.readline().split()

        # Read variable names.  Preserve or destroy case
        # via keyword argument selection:
        if keep_case:
            names = infile.readline().split()
        else:
            names = infile.readline().lower().split()

        # Now that we know the number of variables, we can properly handle
        # the headline and units based on the kwarg *header*:
        pbdat.attrs['header']=headline
        if header == 'units':
            # If headline is just units:
            units = headline.split()
        else:
            # If headline is NOT just units, create blank units:
            units = [''] * (len(names)-npar)

        # For some reason, there are often more units than variables
        # in these files.  It looks as if there are more grid units
        # than grid vectors (e.g. 'R R R' implies X, Y, and Z data
        # in file but only X and Y are present.)  Let's try to work
        # around this rather egregious error.
        nSkip=len(units)+npar-len(names)
        if nSkip<0: nSkip=0

        # Save grid names (e.g. 'x' or 'r') and save associated params.
        pbdat['grid'].attrs['dims']=tuple(names[0:ndim])
        for name, para in zip(names[(nvar+ndim):], para):
            pbdat.attrs[name]=para

        # Create containers for the rest of the data:
        for v, u in zip(names, units[nSkip:]):
            pbdat[v] = dmarray(np.zeros(npts), {'units':u})

        # Load grid points and data:
        for i, line in enumerate(infile.readlines()):
            parts=line.split()
            for j, p in enumerate(parts):
                pbdat[names[j]][i] = p

        # Close the file:
        infile.close()

        # Arrange data into multidimentional arrays if necessary.
        gridnames = names[:ndim]
        if gtyp == 'Irregular':
            for v in names:
                if v not in pbdat: continue
                pbdat[v] = dmarray(np.reshape(pbdat[v], pbdat['grid'], order='F'),
                                   attrs=pbdat[v].attrs)
        elif gtyp == 'Regular':
            # Put coords into vectors:
            prod = [1]+pbdat['grid'].cumprod().tolist()
            for i,x in enumerate(pbdat['grid'].attrs['dims']):
                pbdat[x] = dmarray(pbdat[x][0:prod[i+1]-prod[i]+1:prod[i]],
                    attrs=pbdat[x].attrs)
            for v in names:
                if v not in pbdat.keys(): continue
                if v not in pbdat['grid'].attrs['dims']:
                    pbdat[v] = dmarray(np.reshape(pbdat[v], pbdat['grid'],
                                                  order='F'), attrs=pbdat[v].attrs)

    def readarray(f, dtype=np.float32, inttype=np.int32):
        '''
        Read an array from an unformatted binary file written out by a
        Fortran program.

        Parameters
        ----------
        f : Binary file object
            The file from which to read the array of values.

        Other Parameters
        ----------------
        dtype : data type
            The data type used for data conversion.
        inttype : Numpy integer type
            Set the precision for the integers that store the size of each
            entry.   Defaults to numpy.int32

        Returns
        -------
        numpy.array : Numpy array
            Return the data read from file as a 1-dimensional array.

        '''

        if dtype is str:
            dtype_size_bytes = 1
        else:
            dtype_size_bytes = dtype.itemsize

        # Get the record length
        rec_len = np.fromfile(f, dtype=inttype, count=1)

        # Check that the record length is consistent with the data type
        if rec_len % dtype_size_bytes != 0:
            raise ValueError(
                'Read error: Data type inconsistent with record length (data ' +
                'type size is {0:d} bytes, record length is {1:d} bytes'.format(
                    int(dtype_size_bytes), int(rec_len)))

        if len(rec_len) == 0:
            # Zero-length record...file may be truncated
            raise EOFError('Zero-length read at start marker')

        try:
            startpos = f.tell()
        except IOError:
            seekable = False
        else:
            seekable = True
        if seekable:
            f.seek(0, 2)
            endpos = f.tell()
            if endpos - startpos < (int(rec_len[0]) + np.dtype(inttype).itemsize):
                raise EOFError('File is shorter than expected data')
            f.seek(startpos + int(rec_len[0]), 0)
            rec_len_end = np.fromfile(f, dtype=inttype, count=1)
            f.seek(startpos, 0)
            if rec_len_end != rec_len:
                raise ValueError((
                    'Read error: End marker length ({0:d}) does not match start '
                    'marker length ({1:d}).').format(rec_len[0], rec_len_end[0]) +
                    'This indicates incorrect endiannes, wrong file type, '
                    'or file is corrupt.')
        # Read the data
        if dtype is str:
            A = f.read(rec_len[0])
        else:
            A = np.fromfile(f, dtype=dtype, count=int(rec_len[0]/dtype_size_bytes))

        # Check the record length marker at the end
        rec_len_end = np.fromfile(f, dtype=inttype, count=1)
        if len(rec_len_end) == 0:
            # Couldn't read, file may be truncated
            raise EOFError('Zero-length read at end marker')

        if rec_len_end != rec_len:
            # End marker is inconsistent with start marker. Something is wrong.
            raise ValueError(
                'Read error: End marker does not match start marker ' +
                '({0:d} vs. {1:d} bytes).'.format(int(rec_len), int(rec_len_end)) +
                '  This indicates incorrect endiannes, wrong file type, ' +
                'or file is corrupt')

        return A

    def _skip_entry(f, inttype):
        '''
        Given a binary IDL-formmatted file opened as a file object, *f*,
        and whose file pointer is positioned at the start of an entry,
        skip to the end of the entry and return the total number of bytes skipped.

        Fortran90+ binary files wrap entries with integers that state the number
        of bytes of the entry.  This function reads the number of bytes before
        the data entry, skips over the entry, and reads the trailing byte wrapper
        before returning.

        Parameters
        ==========
        f : Binary file object
            The file from which to read the array of values.
        inttype : Numpy integer type
            Set the precision for the integers that store the size of each
            entry with the correct endianess.
        '''
        # Read preceding entry size, in bytes:
        rec_len = np.fromfile(f, dtype=inttype, count=1)[0]

        # Fast-forward over the entry:
        f.seek(rec_len, 1)

        # Read the trailing entry size, check for match:
        rec_len_end = np.fromfile(f, dtype=inttype, count=1)[0]
        if rec_len_end != rec_len:
            raise IOError('Record length mismatch.')

        # Return total number of bytes skipped:
        return rec_len+2*inttype.itemsize

    def _scan_bin_header(f, endchar, inttype, floattype):
        '''
        Given a binary IDL-formmatted file opened as a file object, *f*,
        and whose file pointer is positioned at the start of the header,
        gather some header information and return to caller.

        The file object's pointer will be set to the end of the whole entry
        (header plus data will be skipped.)

        Parameters
        ----------
        f : Binary file object
            The file from which to read the array of values.
        endchar : str
            Endian character:'<' or '>'
        inttype : Numpy integer type
            Set the precision for the integers that store the size of each
            entry with the correct endianess.
        floattype : numpy float type
            The data type used for data conversion with the correct endianess.

        Returns
        -------
        info : dict
            A dictionary with the *start* and *end* bytes of the record, time
            information including the *iter*ation, *ndim* number of dimensions,
            *npar* number of parameters, *nvar* number of variables, and
            simulation *runtime* in seconds.
        '''

        # Get starting location in file:
        rec_start = f.tell()

        # Create a dictionary to store the info from the file:
        info = {'start':rec_start}

        # Read initial header:
        headline = readarray(f, str, inttype)
        headline = headline.decode('utf-8')

        # Construct size of header entries:
        header_fields_dtype = np.dtype([
            ('it', np.int32), ('t', floattype), ('ndim', np.int32),
            ('npar', np.int32), ('nvar', np.int32)])
        header_fields_dtype.newbyteorder(endchar)

        # From header, get iteration, runtime (seconds), number of dimensions,
        # number of parameters, and number of variables in that order.
        # Stash relevant values into the "info" dict:
        vals = readarray(f, dtype=header_fields_dtype, inttype=inttype)[0]
        for v, x in zip(['iter', 'runtime', 'ndim', 'nparam', 'nvar'], vals):
            info[v] = x

        # Dimensionality may be negative to indicate non-uniform grid:
        info['ndim'] = abs(info['ndim'])

        # Get gridsize:
        grid = dmarray(readarray(f, inttype, inttype))
        npoints = abs(grid.prod())

        # Set the size of floating points in bytes:
        nbytes = floattype.itemsize

        # Skip the rest of the header:
        if info['nparam'] > 0:
            _skip_entry(f, inttype)  # Skip parameters.
        _skip_entry(f, inttype)      # Skip variable names.

        # Get header size in bytes:
        head_size = f.tell() - rec_start

        # Calculate the end point of the data frame
        # end point = start + header + wrapper bytes + data size):
        info['end'] = info['start'] + head_size +  \
            2*inttype.itemsize*(1+info['nvar']) + \
            nbytes*(info['nvar']+info['ndim'])*npoints

        # Jump to end of current data frame:
        f.seek(info['end'], 0)

        return info

    def _probe_idlfile(filename):
        '''
        For an SWMF IDL-formatted output file, probe the header to determine if the
        file is ASCII or binary formatted.  If binary, attempt to determine the
        byte ordering (i.e., endianess) and size of floating point values.

        Parameters
        ----------
        filename : str
            String path of file to probe.

        Other Parameters
        ----------------
        None

        Returns
        -------
        fmt : str
            Format of file, either "asc" or "bin" for ASCII or binary, respectively.

        endian : str
            Binary byte ordering, either '<' or '>' for little or big endianess,
            respectively (None for ASCII).

        inttype : numpy.dtype
            A numpy data type representing the size of the file's integers with
            the appropriate byte ordering.  Returns None for ASCII files.

        floattype : numpy.dtype
            A numpy data type representing the size of the file's floating point
            values with the appropriate byte ordering.  Returns None for ASCII
            files.

        '''

        # Set default guesses:
        endian    = '<'
        inttype   = np.dtype(np.int32)
        floattype = np.dtype(np.float32)

        with open(filename, 'rb') as f:
            # On the first try, we may fail because of wrong-endianess.
            # If that is the case, swap that endian and try again.
            inttype.newbyteorder(endian)

            try:
                # Try to parse with little endian byte ordering:
                headline = readarray(f, str, np.int32)
            except (ValueError, EOFError):
                # On fail, rewind and try with big endian byte ordering:
                endian = '>'
                inttype.newbyteorder(endian)
                f.seek(0)
                # Attempt to read and parse the header line again:
                # If we fail here, almost certainly an ascii file.
                try:
                    headline = readarray(f, str,)
                    headline = headline.decode('utf-8')
                except (ValueError, EOFError):
                    return 'asc', False, False, False

            # detect double-precision file.
            pos = f.tell()
            RecLen = np.fromfile(f, dtype=inttype, count=1)
            f.seek(pos)

            # Set data types
            if RecLen > 20:
                floattype = np.dtype(np.float64)
            floattype.newbyteorder(endian)

        return 'bin', endian, inttype, floattype

    def _read_idl_bin(pbdat, header='units', start_loc=0, keep_case=True,
                      headeronly=False):
        '''
        Load a SWMF IDL binary output file and load into a pre-existing PbData
        object.  This should only be called by :class:`IdlFile`, which will
        include information on endianess and size of integers & floating point
        values.

        The input object must have the name of the file to be opened and read
        stored in its attributes list and named 'file'.

        The kwarg *header* dictates how the header line will be handled.  In some
        output files, the header is simply the list of units.  In others, it
        contains other data.  If *header* is set to 'units', the header is
        assumed to be a list of units for each variable contained within the
        file.  If set to **None** or not recognized, the header will be saved
        in the object's attribute list under 'header'.

        Parameters
        ----------
        pbdat : PbData object
            The object into which the data will be loaded.

        Other Parameters
        ----------------
        header : string or **None**
            A string indicating how the header line will be handled; see above.
        start_loc : int
            Location to start reading inside the file as number of bytes.  This is
            used to set the starting position of a given data frame for *.outs
            files.  Default is zero (read at beginning of file).
        keep_case : boolean
            If set to True, the case of variable names will be preserved.  If
            set to False, variable names will be set to all lower case.

        Returns
        -------
        True : Boolean
            Returns True on success.

        '''

        # Some convenience variables:
        endchar, inttype, floattype = pbdat._endchar, pbdat._int, pbdat._float

        # Open, read, and parse the file into numpy arrays.
        # Note that Fortran writes integer buffers around records, so
        # we must parse those as well.
        with open(pbdat.attrs['file'], 'rb') as infile:
            # Jump to start_loc:
            infile.seek(start_loc, 0)

            # Read header information.
            headline = readarray(infile, str, inttype)
            headline = headline.decode('utf-8')

            # Parse rest of header
            header_fields_dtype = np.dtype([
                ('it', np.int32), ('t', floattype), ('ndim', np.int32),
                ('npar', np.int32), ('nvar', np.int32)])
            header_fields_dtype.newbyteorder(endchar)

            (pbdat.attrs['iter'], pbdat.attrs['runtime'],
             pbdat.attrs['ndim'], pbdat.attrs['nparam'], pbdat.attrs['nvar']) = \
                readarray(infile,
                          dtype=header_fields_dtype,
                          inttype=inttype)[0]

            # Get gridsize
            pbdat['grid'] = dmarray(readarray(infile, inttype, inttype))

            # Data from generalized (structured but irregular) grids can be
            # detected by a negative ndim value.  Unstructured grids (e.g.
            # BATS, AMRVAC) are signified by negative ndim values AND
            # the grid size is always [x, 1(, 1)]
            # Here, we set the grid type attribute to either Regular,
            # Generalized, or Unstructured.  Let's set that here.
            pbdat['grid'].attrs['gtype'] = 'Regular'
            pbdat['grid'].attrs['npoints'] = abs(pbdat['grid'].prod())
            if pbdat.attrs['ndim'] < 0:
                if any(pbdat['grid'][1:] > 1):
                    pbdat['grid'].attrs['gtype'] = 'Generalized'
                else:
                    pbdat['grid'].attrs['gtype'] = 'Unstructured'
                    pbdat['grid'].attrs['npoints'] = pbdat['grid'][0]
            pbdat.attrs['ndim'] = abs(pbdat.attrs['ndim'])

            # Quick ref vars:
            gtyp = pbdat['grid'].attrs['gtype']
            npts = pbdat['grid'].attrs['npoints']
            ndim = pbdat['grid'].size
            nvar = pbdat.attrs['nvar']
            npar = pbdat.attrs['nparam']

            # Read parameters stored in file.
            para  = np.zeros(npar)
            if npar > 0:
                para[:] = readarray(infile, floattype, inttype)

            names = readarray(infile, str, inttype).decode('utf-8')

            # Preserve or destroy original case of variable names:
            if not keep_case: names = names.lower()

            names.strip()
            names = names.split()

            # Now that we know the number of variables, we can properly handle
            # the headline and units based on the kwarg *header*:
            pbdat.attrs['header'] = headline
            if header == 'units':
                # If headline is just units:
                units = headline.split()
            else:
                # If headline is NOT just units, create blank units:
                units = [''] * (len(names) - npar)

            # For some reason, there are often more units than variables
            # in these files.  It looks as if there are more grid units
            # than grid vectors (e.g. 'R R R' implies X, Y, and Z data
            # in file but only X and Y are present.)  Let's try to work
            # around this curiousity:
            nSkip = len(units) + npar - len(names)
            if nSkip < 0: nSkip = 0

            # Save grid names (e.g. 'x' or 'r') and save associated params.
            pbdat['grid'].attrs['dims'] = tuple(names[0:ndim])
            for name, para in zip(names[(nvar+ndim):], para):
                pbdat.attrs[name] = para

            # Get the grid points...
            prod = [1] + pbdat['grid'].cumprod().tolist()

            # Read the data into a temporary array
            griddata = readarray(infile, floattype, inttype)
            for i in range(0, ndim):
                # Get the grid coordinates for this dimension
                tempgrid = griddata[npts*i:npts*(i+1)]

                # Unstructred grids get loaded as vectors.
                if gtyp == 'Unstructured':
                    pbdat[names[i]] = dmarray(tempgrid)
                # Irregularly gridded items need multidimensional grid arrays:
                elif gtyp == 'Irregular':
                    pbdat[names[i]] = dmarray(np.reshape(tempgrid, pbdat['grid'],
                                                         order='F'))
                # Regularly gridded ones need vector grid arrays:
                elif gtyp == 'Regular':
                    pbdat[names[i]] = dmarray(np.zeros(pbdat['grid'][i]))
                    for j in range(int(pbdat['grid'][i])):
                        pbdat[names[i]][j] = tempgrid[j*int(prod[i])]
                else:
                    raise ValueError('Unknown grid type: {0}'.format(pbdat.gridtype))
                # Add units to grid.
                if units: pbdat[names[i]].attrs['units'] = units.pop(nSkip)

            # Get the actual data and sort.
            for i in range(ndim, nvar+ndim):
                pbdat[names[i]] = dmarray(readarray(infile, floattype, inttype))
                if units: pbdat[names[i]].attrs['units'] = units.pop(nSkip)
                if gtyp != 'Unstructured':
                    # Put data into multidimensional arrays.
                    pbdat[names[i]] = pbdat[names[i]].reshape(pbdat['grid'], order='F')

            # Unstructured data can be in any order, so let's sort it.
            if gtyp == 'Unstructured':
                gridtotal = np.zeros(npts)
                offset = 0.0  # The offset ensures no repeating vals while sorting.
                for key in pbdat['grid'].attrs['dims']:
                    gridtotal = gridtotal + offset + pbdat[key]
                    offset = offset + np.pi/2.0
                    SortIndex = np.argsort(gridtotal)
                for key in list(names[:nvar+ndim]):
                    if key == 'grid': continue
                    pbdat[key] = pbdat[key][SortIndex]

    class PbData(SpaceData):
        '''
        The base class for all PyBats data container classes.  Inherits from
        :class:`spacepy.datamodel.SpaceData` but has additional methods for quickly
        exploring an SWMF dataset.

        Just like :class:`spacepy.datamodel.SpaceData` objects, *PbData* objects
        work just like dictionaries except they have special **attr** dictionary
        attributes for both the top-level object and most values.  This means that
        the following syntax can be used to explore a generic *PbData* object:

        >>>print obj.keys()
        >>>print obj.attrs
        >>>value = obj[key]

        Printing *PbData* objects will produce a tree of contents and attributes;
        calling ``self.listunits()`` will print all values that have the 'units'
        attribute and the associated units.  Hence, it is often most instructive
        to use the following two lines to quickly learn a *PbData*'s contents:

        >>>print obj
        >>>obj.listunits()

        *PbData* is the main organizational tool for Pybats datasets, so the
        information here is applicable to nearly all Pybats classes.
        '''

        def __init__(self, *args, **kwargs):
            super(PbData, self).__init__(*args, **kwargs)  # Init as SpaceData.

        def __repr__(self):
            return 'PyBats data object'

        def __str__(self):
            '''
            Display contents of container.
            '''
            print(type(self))
            self.tree(attrs=True, verbose=True)
            return ''

        def listunits(self):
            '''
            List all variables and associated units.
            '''
            keys = list(self.keys())
            keys.sort()
            length = 0
            for key in keys:
                if len(key) > length: length=len(key)
            form = "%%%is:%%s"%length
            for key in keys:
                if 'units' in self[key].attrs:
                    print(form%(key, self[key].attrs['units']))

        def timeseries_append(self, obj):
            '''
            If *obj* is of the same type as self, and both have a 'time'
            entry, append all arrays within *obj* that have the same size as
            'time' to the corresponding arrays within self.  This is useful
            for combining time series data of consecutive data.
            '''

            # check to make sure that the append appears feasible:
            if type(obj) != type(self):
                raise TypeError('Cannot append type {} to type {}.'.format(
                    type(obj), type(self)))
            if 'time' not in obj:
                raise ValueError('No time vector in {} object'.format(type(obj)))
            if 'time' not in self:
                raise ValueError('No time fector in {} object'.format(type(self)))

            npts = self['time'].size

            for v in self:
                # Only combine vectors that are the same size as time and are
                # in both objects.
                if v not in obj: continue
                if self[v].size != npts: continue
                # Append away.
                self[v] = dmarray(np.append(self[v], obj[v]), self[v].attrs)

    class IdlFile(PbData):

        '''
        Introduction
        ------------

        An object class that reads/parses an IDL-formatted output file from the
        SWMF and places it into a :class:`spacepy.pybats.PbData` object.

        Usage:
        >>>data = spacepy.pybats.IdlFile('binary_file.out')

        See :class:`spacepy.pybats.PbData` for information on how to explore
        data contained within the returned object.

        This class serves as a parent class to SWMF component-specific derivative
        classes that do more preprocessing of the data before returning the
        object.  Hence, using this class to read binary files is typically not
        the most efficient way to proceed.  Look for a PyBats sub module that suits
        your specific needs, or use this base object to write your own.

        Multi-Frame Files
        -----------------

        Typically, Idl-formatted data has a single *frame*, or a single snapshot
        worth of data (a `*.out` file).  However, it is possible to (externally)
        combine many of these files together such that a time series of data frames
        are contained within a single file (`*.outs` files). This class can read
        these files, but only one data frame can be made available at a time.  This
        prevents very large memory requirements.

        These files are opened and handled similarly to regular IDL-formatted
        files  with some important differences.  Upon instantiation, the user
        may select which data frame to open with the *iframe* kwarg.  The default
        action is to open the first frame.  The user may learn more about the
        number of frames within the file and their associated epoch/iteration
        information by examining the top level *attrs* (see below.)
        The user may switch to any arbitrary frame using the `switch_frame(iframe)`
        object method.  This will load the relevant data from disk into the
        object, overwriting the previous contents.

        If the user has created any new variables using the data from a certain
        data frame, those new values will not be updated automatically.  An
        exception is for any *self.calc_* type methods that are set to update
        automatically.

        Top Level Attributes
        --------------------
        Critical file information can be found via the top-level object attributes
        (e.g., accessing the dictionary `self.attrs`).  All IdlFile objects and
        child classes begin with at least these attributes:

        | Attribute Name       | Description                                        |
        | -------------------- | -------------------------------------------------- |
        | file                 | Path/name of file represented by object            |
        | iter/time/runtime    | Iteration/datetime/runtime of the current frame    |
        | iters/times/runtimes | Lists of all iterations/times of each data frame   |
        | *_range              | The range of iterations/epochs covered in the file |
        | ndim                 | Number of spatial dimensions covered by the data   |
        | nframe               | The total number of data frames within the file    |
        | iframe               | The current frame loaded (zero-based)              |
        | format               | The format of the file, either binary or ascii     |
        | header               | The raw string header of the file                  |

        Notes
        -----
        PyBats assumes little endian byte ordering because
        this is what most machines use.  However, there is an autodetect feature
        such that, if PyBats doesn't make sense of the first read (a record length
        entry, or RecLen), it will proceed using big endian ordering.  If this
        doesn't work, the error will manifest itself through the "struct" package
        as an "unpack requires a string of argument length 'X'".

        Parameters
        ----------
        filename : string
            A *.out or *.outs SWMF output file name.

        Other Parameters
        ----------------
        header : str or **None**
            Determine how to interpret the additional header information.
            Defaults to 'units'.

        keep_case : boolean
            If set to True, the case of variable names will be preserved.  If
            set to False, variable names will be set to all lower case.
        '''

        def __init__(self, filename, iframe=0, header='units',
                     keep_case=True, *args, **kwargs):
            super(IdlFile, self).__init__(*args, **kwargs)  # Init as PbData.

            # Gather information about the file: format, endianess (if necessary),
            # number of picts/frames, etc.:
            fmt, endchar, inttype, floattype = _probe_idlfile(filename)
            self.attrs['file']   = filename   # Save file name.
            self.attrs['format'] = fmt        # Save file format.

            # Gather information about time range of file from name:
            t_info = list(parse_filename_time(filename))
            for i in range(len(t_info)):
                if type(t_info[i]) != list: t_info[i]=[t_info[i]]
            self.attrs['iter_range']    = t_info[0]
            self.attrs['runtime_range'] = t_info[1]
            self.attrs['time_range']    = t_info[2]

            # For binary files, store information about file:
            self._endchar, self._int, self._float = endchar, inttype, floattype

            # Save file interpretation options tucked as protected attributes:
            self._header, self._keep_case = header, keep_case

            # Collect information about the number of epoch frames in the file:
            if fmt=='bin':
                self._scan_bin_frames()
            else:
                self._scan_asc_frames()

            # Read one entry of the file (defaults to first frame):
            self.read(iframe=iframe)      # Read file.

            # Update information about the currently loaded frame.
            self.attrs['iframe'] = iframe
            self.attrs['time']   = self.attrs['times'][iframe]

            # Create string representation of run time:
            time = self.attrs['runtime']  # Convenience variable.
            self.attrs['strtime'] = "{:04.0f}h{:02.0f}m{:06.3f}s".format(
                np.floor(time//3600), np.floor(time % 3600//60.0), time % 60.0)

        def _scan_bin_frames(self):
            '''
            Open the binary-formatted file associated with *self* and scan all
            headers to count the number of epoch frames and details of each.
            Results are stored within *self*.
            '''

            from datetime import timedelta as tdelt

            # Create some variables to store information:
            nframe = 0  # Number of epoch frames in file.
            iters, runtimes = [], []  # Lists of time information.
            offset = []  # Byte offset from beginning of file of each frame.

            with open(self.attrs['file'], 'rb') as f:
                f.seek(0, 2)  # Jump to end of file (in Py3, this returns location)
                file_size = f.tell()  # Get number of bytes in file.
                f.seek(0)  # Rewind to file start.

                # Loop over all data frames and collect information:
                while f.tell() < file_size:
                    info = _scan_bin_header(f, self._endchar, self._int, self._float)
                    # Stash information into lists:
                    offset.append(info['start'])
                    iters.append(info['iter'])
                    runtimes.append(info['runtime'])
                    nframe += 1

            # Store everything as file-level attrs; convert to numpy arrays.
            self.attrs['nframe'] = nframe
            self.attrs['iters'] = np.array(iters)
            self.attrs['runtimes'] = np.array(runtimes)

            # Use times info to build datetimes and update file-level attributes.
            if self.attrs['time_range'] != [None]:
                self.attrs['times'] = np.array(
                    [self.attrs['time_range'][0]+tdelt(seconds=int(x-runtimes[0])) for x in runtimes])
            else:
                self.attrs['times'] = np.array(nframe*[None])

            # Ensure all ranges are two-element arrays and update using
            # information we gathered from the header.
            if self.attrs['iter_range'] == [None]:
                self.attrs['iter_range'] = [iters[0], iters[-1]]
            if self.attrs['runtime_range'] == [None]:
                self.attrs['runtime_range'] = [runtimes[0], runtimes[-1]]

            # Stash the offset of frames as a private attribute:
            self._offsets = np.array(offset)

        def _scan_asc_frames(self):
            '''
            Open the ascii-formatted file associated with *self* and scan all
            headers to count the number of epoch frames and details of each.
            Results are stored within *self*.

            This is a placeholder only until ascii-formatted .outs files are
            fully supported.
            '''

            # Only use top-level frame as of now.
            self._offsets = np.array([0])
            self.attrs['nframe']   = 1
            self.attrs['iters']    = [0,0]
            self.attrs['runtimes'] = [0,0]
            self.attrs['times']    = [0,0]

        def switch_frame(self, iframe):
            '''
            For files that have more than one data frame (i.e., `*.outs` files),
            load data from the *iframe*-th frame into the object replacing what is
            currently loaded.
            '''

            # Ensure that given frame exists within object:
            if iframe < -self.attrs['nframe'] or iframe >= self.attrs['nframe']:
                raise IndexError("iframe {} is outside range of [0,{})".format(
                    iframe, self.attrs['nframe']))

            self.read(iframe)

            # Update information about the current frame:
            self.attrs['iframe'] = self.attrs['nframe'] + iframe if iframe < 0 else iframe
            self.attrs['iter']   = self.attrs['iters'][iframe]
            self.attrs['runtime']= self.attrs['runtimes'][iframe]
            self.attrs['time']   = self.attrs['times'][iframe]

            # Update string time:
            time = self.attrs['runtime']  # Convenience variable.
            self.attrs['strtime'] = "{:04.0f}h{:02.0f}m{:06.3f}s".format(
                np.floor(time//3600), np.floor(time % 3600//60.0), time % 60.0)

            # Re-do calculations if any have been done.
            if hasattr(self, '_calcs'):
                # Get dictionary of name-method pairs for called calculations:
                calcs = self._calcs
                # Reset dict of called calculations:
                self._calcs = {}
                # Call all calculation methods:
                for name in calcs:
                    args, kwargs = calcs[name]
                    getattr(self, name)(*args, **kwargs)

        def __repr__(self):
            return 'SWMF IDL-Binary file "%s"' % (self.attrs['file'])

        def read(self, iframe=0):
            '''
            This method reads an IDL-formatted BATS-R-US output file and places
            the data into the object.  The file read is self.filename which is
            set when the object is instantiation.
            '''

            # Get location of frame that we wish to read:
            loc = self._offsets[iframe]

            if self.attrs['format'] == 'asc':
                _read_idl_ascii(self, header=self._header, start_loc=loc,
                                keep_case=self._keep_case)
            elif self.attrs['format'] == 'bin':
                _read_idl_bin(self, header=self._header, start_loc=loc,
                              keep_case=self._keep_case)
            else:
                raise ValueError('Unrecognized file format: {}'.format(
                    self._format))

    class LogFile(PbData):
        ''' An object to read and handle SWMF-type logfiles.

        *LogFile* objects read and hold all information in an SWMF
        ascii time-varying logfile.  The file is read upon instantiation.
        Many SWMF codes
        produce flat ascii files that can be read by this class; the most
        frequently used ones have their own classes that inherit from this.

        See :class:`spacepy.pybats.PbData` for information on how to explore
        data contained within the returned object.

        Usage:
        >>>data = spacepy.pybats.LogFile('filename.log')

        =========  ===========================================
        kwarg      Description
        ---------  -------------------------------------------
        starttime  Manually set the start time of the data.
        =========  ===========================================

        Time is handled by Python's datetime package.  Given that time
        may or may not be given in the logfile, there are three options
        for how time is returned:

            1. if the full date and time are listed in the file, ``self['time']``
            is an array of datetime objects corresponding to the entries.  The
            starttime kwarg is ignored.

            2. If only the runtime (seconds from start of simulation) is given,
            self['time'] is an array of datetime objects that starts from
            the given *starttime* kwarg which defaults to  1/1/1 00:00UT.

            3. If neither of the above are given, the time is assumed to
            advance one second per line starting from either the starttime kwarg
            or from 2000/1/1 00:00UT + the first iteration (if given in file.)
            As you can imagine, this is sketchy at best.

        This time issue is output dependent: some SWMF models place the full date
        and time into the log by default while others will almost never include the
        full date and time.  The variable ``self['runtime']`` contains the more
        generic seconds from simulation start values.

        Example usage:

        >>> import spacepy.pybats as pb
        >>> import pylab as plt
        >>> import datetime as dt
        >>> time1 = dt.datetime(2009,11,30,9,0)
        >>> file1 = pb.logfile('satfile_n000000.dat', starttime=time1)
        >>> plt.plot(file1['time'], file1['dst'])

        '''

        import datetime as dt

        def __init__(self, filename, starttime=(2000,1,1,0,0,0), keep_case=True,
                     *args, **kwargs):
            super(LogFile, self).__init__(*args, **kwargs)
            self.attrs['file'] = filename
            self.read(starttime, keep_case)

        def read(self, starttime, keep_case=True):
            '''
            Load the ascii logfile located at self.filename.
            This method is automatically called upon instantiation.
            '''
            import numpy as np
            import datetime as dt

            # Convert starttime from tuple to datetime object.
            if type(starttime) != dt.datetime:
                if len(starttime) != 6:
                    raise ValueError('starttime must be a length 6 Tuple ' +
                                     'or a datetime.datetime object')
                starttime=dt.datetime(starttime[0], starttime[1], starttime[2],
                                      starttime[3], starttime[4], starttime[5])

            # Slurp in entire file.
            infile = open(self.attrs['file'], 'r')
            raw = infile.readlines()
            infile.close()

            # Parse the header.
            self.attrs['descrip'] = raw.pop(0)
            raw_names = raw.pop(0)
            if not keep_case: raw_names = raw_names.lower()
            names = raw_names.split()
            loc={}
            # Keep track of in which column each data vector lies.
            for i, name in enumerate(names):
                loc[name] = i

            # Use the header info to set up arrays
            # for time, iteration, and the data.
            npts = len(raw)
            # If opening an incomplete file, we must skip the last line.
            if len(raw[-1].split()) < len(names):
                npts=npts-1
            self.attrs['npts']=npts

            # Pop time/date/iteration names off of Namevar.
            for key in ['year','mo','dy','hr','mn','sc','msc',
                        't','it','yy','mm','dd','hh','ss','ms']:
                if key in loc: names.pop(names.index(key))

            # Create containers for data:
            time=dmarray(np.zeros(npts, dtype=object))
            runtime=dmarray(np.zeros(npts), attrs={'units':'s'})
            self['iter']=dmarray(np.zeros(npts))
            for name in names:
                self[name] = dmarray(np.zeros(npts))

            for i in range(npts):
                vals = raw[i].split()
                # Set time:
                if 'year' in loc or 'yy' in loc:
                    # If "year" or "yy" is listed, we have the full datetime.
                    if 'year' in loc:
                        # BATS date format
                        time[i]=(dt.datetime(
                                int(vals[loc['year']]), # Year
                                int(vals[loc['mo']  ]), # Month
                                int(vals[loc['dy']]), # Day
                                int(vals[loc['hr']]), # Hour
                                int(vals[loc['mn']]), # Minute
                                int(vals[loc['sc']]), # Second
                                int(vals[loc['msc']]) * 1000 #microsec
                                ))
                    elif 'yy' in loc:
                        # RIM date format
                        time[i]=(dt.datetime(
                                int(vals[1]), # Year
                                int(vals[2]), # Month
                                int(vals[3]), # Day
                                int(vals[4]), # Hour
                                int(vals[5]), # Minute
                                int(vals[6]), # Second
                                int(vals[7]) * 1000 #microsec
                                ))
                    diffT = time[i] - time[0]
                    runtime[i]=diffT.days*24.0*3600.0 + \
                        diffT.seconds + \
                        diffT.microseconds*1E-6
                elif 't' in loc:
                    # If "t" but no "year" is listed, we only have runtime.
                    # Check to ensure number of seconds doesn't
                    # exceed 24 hours.
                    nowsecs = float(vals[loc['t']])
                    nowdays = int(nowsecs / (24.0 * 3600.0))
                    nowsecs = nowsecs - (24.0 * 3600.0 * nowdays)
                    delta = dt.timedelta(\
                        days    = nowdays,
                        seconds = nowsecs
                        )
                    newtime = starttime + delta
                    time[i]=newtime
                    runtime[i]=nowdays*24.0*3600.0 + nowsecs
                elif 'it' in loc:
                    # Check to ensure number of seconds doesn't
                    # exceed 24 hours.
                    nowsecs = float(vals[loc['it']])
                    nowdays = int(nowsecs / (24.0 * 3600.0))
                    nowsecs = nowsecs - (24.0 * 3600.0 * nowdays)
                    delta = dt.timedelta(\
                        days    = nowdays,
                        seconds = nowsecs
                        )
                    newtime = starttime + delta
                    time[i]=newtime
                    runtime[i]=nowdays*24.*3600.+nowsecs
                else:
                    time[i]=starttime + dt.timedelta(float(i))
                # Set iteration:
                if 'it' in loc:
                    if '*' in vals[loc['it']]:
                        self['iter'][i] = -1
                    else:
                        self['iter'][i] = int(vals[loc['it']])
                else: self['iter'][i] = i
                # Collect data
                for j, name in enumerate(names):
                    self[name][i] = float(vals[loc[name]])

            # Convert time and runtime to dmarrays.
            self['time'] = time
            self['runtime'] = runtime

    class NgdcIndex(PbData):
        '''
        Many models incorporated into the SWMF rely on National Geophysical Data
        Center (NGDC) provided index files (especially F10.7 and Kp).  These
        files, albeit simple ascii, have a unique format and expansive header
        that can be cumbersome to handle.  Data files can be obtained from
        http://spidr.ngdc.noaa.gov .

        NgdcIndex objects aid in reading, creating, and visualizing these files.

        Creating an :class:`NgdcIndex` object is simple:

        >>> from spacepy import pybats
        >>> obj=pybats.NgdcIndex(filename='ngdc_example.dat')

        Upon instantiation, if *filename* is a valid file AND kwarg *load* is set
        to boolean True, the contents of *filename* are loaded into the object
        and no other work needs to be done.

        If *filename* is False or *load* is False, a blank :class:`NgdcIndex`
        is created for the user to manipulate.
        The user can set the time array and the ssociated data values to any values
        desired and use the method *obj.write()* to dump the contents to an NGDC
        formatted input file.  See the documentation for the write method for
        more details.

        This class is a work-in-progress.  It is especially tuned for SWMF-needs
        and cannot be considered a general function for the handling of generic
        NGDC files.

        =========== ============================================================
        Kwarg       Description
        ----------- ------------------------------------------------------------
        filename    Set the input/output file name.
        load        Read file upon instantiation?  Defaults to **True**
        =========== ============================================================

        '''

        def __init__(self, filename=None, load=True, *args, **kwargs):

            super(NgdcIndex, self).__init__(*args, **kwargs)
            # Set key information.
            self.attrs['file'] = filename
            self.attrs['comments'] = []

            if load:
                self._read()

        def _read(self):
            '''
            Read an existing NGDC file into memory.  Should only be called upon
            instantiation.
            '''
            from dateutil.parser import parse

            # Start by reading the header.
            infile = open(self.attrs['file'], 'r')
            temp = infile.readline()
            while temp != '#'+50*'-'+'\n': #Detect start of file.
                # Skip blank comments, save substantive ones.
                if temp == '#\n':
                    temp = infile.readline()
                    continue
                self.attrs['comments'].append(temp)
                temp = infile.readline()

            # Skip "data start" marker.
            infile.readline()

            # Continue to read and sort all data in file.
            def read_one_var():
                attrs = {}
                # Get current variable.  Strip out all but name.
                varname = infile.readline().split(':')[-1].strip()
                temp = infile.readline()
                while temp != '#>\n':
                    if temp != '#\n':
                        parts = temp[1:].split(':')
                        attrs[parts[0].strip()] = parts[-1].strip()
                    temp = infile.readline()

                # Create some dummy arrays.
                t = []
                d = []

                # Skip the sub-header.
                temp = infile.readline()
                temp = infile.readline()

                # Parse data until next variable or EOF.
                while (temp != '#>\n') and (temp != ''):
                    parts = temp.split()
                    t.append(parse(' '.join(parts[0:2])))
                    d.append(float(parts[2]))
                    temp = infile.readline()

                # Put the variable together.
                self[varname] = dmarray([t, d], attrs=attrs)

                # Make judgement as to whether there is more data or not.
                return (temp == '#>\n')

            IsData = True
            while IsData:
                IsData = read_one_var()

        def write(self, outfile=False):
            '''
            Write the :class:`NgdcIndex` object to file.  Kwarg *outfile* can be
            used to specify the path of the output file; if it is not set,
            *self.attrs['file']* is used.  If this is not set, default to
            "ngdc_index.dat".
            '''

            import datetime as dt

            if not outfile:
                if self.attrs['file']!=None:
                    outfile=self.attrs['file']
                else:
                    outfile='ngdc_index.dat'

            out = open(outfile, 'w')

            # Write header.
            for c in self.attrs['comments']:
                out.write(c)
            out.write(2*'#\n'+'#'+50*'-'+'\n')

            # Write variables.
            for k in self:
                out.write('#>\n')
                out.write('#%s: %s\n' % ('Element', k))
                for a in self[k].attrs:
                    out.write('#%s: %s\n' % (a, self[k].attrs[a]))
                out.write('#>\n#yyyy-MM-dd HH:mm value qualifier description\n')
                for i in range(len(self[k][0,:])):
                    t = self[k][0,i]; d = self[k][1,i]
                    out.write('%04i-%02i-%02i %02i:%02i%7.1f\t""\t""\n' %
                              (t.year,t.month,t.day,t.hour,t.minute, d))

    class ImfInput(PbData):
        '''
        A class to read, write, manipulate, and visualize solar wind upstream
        input files for SWMF simulations.  More about such files can be found
        in the SWMF/BATS-R-US documentation for the \#SOLARWINDFILE command.

        Creating an :class:`ImfInput` object is simple:

        >>> from spacepy import pybats
        >>> obj=pybats.ImfInput(filename='test.dat', load=True)

        Upon instantiation, if *filename* is a valid file AND kwarg *load* is set
        to boolean True, the contents of *filename* are loaded into the object
        and no other work needs to be done.

        If *filename* is False or *load* is False, a blank :class:`ImfInput file`
        is created for the user to manipulate.
        The user can set the time array and the
        associated data values (see *obj.attrs['var']* for a list) to any values
        desired and use the method *obj.write()* to dump the contents to an SWMF
        formatted input file.  See the documentation for the write method for
        more details.

        Like most :mod:`~spacepy.pybats` objects, you may interact with
        :class:`ImfInput` objects as if they were specialized dictionaries.
        Access data like so:

        >>> obj.keys()
        ['bx', 'by', 'bz', 'vx', 'vy', 'vz', 'rho', 'temp']
        >>> density=obj['rho']

        Adding new data entries is equally simple so long as you have the values
        and the name for the values::

        >>> import numpy as np
        >>> v = np.sqrt(obj['vx']**2 + obj['vy']**2 + obj['vz']**2)
        >>> obj['v']=v

        =========== ============================================================
        Kwarg       Description
        ----------- ------------------------------------------------------------
        filename    Set the input/output file name.
        load        Read file upon instantiation?  Defaults to **True**
        npoints     For empty data sets, sets number of points (default is 0)
        =========== ============================================================
        '''

        def __init__(self, filename=False, load=True, npoints=0, *args, **kwargs):
            from numpy import zeros

            # Initialize data object and required attributes.
            super(ImfInput, self).__init__(*args, **kwargs)
            self.attrs['var']= ['bx', 'by', 'bz', 'ux', 'uy', 'uz', 'rho', 'temp']
            self.attrs['std_var']=True
            self.attrs['coor']='GSM'
            self.attrs['satxyz']=[None, None, None]
            self.attrs['zerobx']=False
            self.attrs['reread']=False
            self.attrs['delay']=None
            self.attrs['plane']=[None, None]
            self.attrs['header']=[]
            self['time']=dmarray(zeros(npoints, dtype=object))

            # Set Filename.
            if filename:
                self.attrs['file'] = filename
            else:
                self.attrs['file'] = None

            # Load/create data vectors.
            if filename and load:  # Load contents from existing file.
                self.read(filename)
            else:
                units   = ['nT', 'nT', 'nT', 'km/s', 'km/s', 'km/s', 'cm^-3', 'K']
                for i, key in enumerate(self.attrs['var']):
                    self[key]=dmarray(zeros(npoints), attrs={'units':units[i]})

            # Determine the density variable, which can either be "n" or "rho".
            if "n" in self.attrs['var']:
                self._denvar="n"
            elif "rho" in self.attrs['var']:
                self._denvar="rho"
            else:
                raise ValueError('Could not find density variable in file.')

            self.calc_pram()

        def calc_pram(self):
            '''
            Calculate ram pressure from SW conditions.  Output units in nPa.
            If object was instantiated via an existing imf file, this value
            is calculated automatically.
            '''
            n = self._denvar

            self['pram']=dmarray(self['ux']**2.*self[n]*1.67621E-6,{'units':'nPa'})

        def calc_u(self):
            '''
            Calculate the magnitude of the total solar wind bulk velocity.  Store
            internally as self['u'].
            '''

            self['u'] = dmarray( np.sqrt(self['ux']**2+self['uy']**2+self['uz']**2),
                                 {'units':'km/s'} )

            return True

        def calc_b(self):
            '''
            Calculate the magnitude of the IMF in nT.  Store as self['b'].
            '''

            self['b'] = dmarray( np.sqrt(self['bx']**2+self['by']**2+self['bz']**2),
                                 {'units':'nT'} )

            return True

        def calc_alf(self):
            '''
            Calculate the solar wind Alfven speed in $km/s$.  Result is stored
            internally as self['vAlf']
            '''

            if 'b'    not in self: self.calc_b()

            # Const: nT->T, m->km, mu_0, proton mass, cm-3->m-3.
            const = 1E-12/np.sqrt(4.*np.pi*10**-7*1.67E-27*100**3)

            self['vAlf']=dmarray(const*self['b']/np.sqrt(self['rho']),
                                {'units':'km/s'})

            return True

        def calc_alfmach(self):
            '''
            Calculate the Alvenic Mach number and save as self['machA'].
            Units default to $km/s$.
            '''

            if 'vAlf' not in self: self.calc_alf()
            if 'u'    not in self: self.calc_u()
            self['machA']=dmarray(self['u']/self['vAlf'], {'units':None})

            return True

        def varcheck(self):
            '''
            Ensure that the variable list, which gives the order of the
            variables as they are written to file, correctly corresponds
            to the variables stored in the ImfInput object.

            Returns True on success, returns warnings and False on fail.
            '''
            # Convenience:
            var=self.attrs['var']
            key=list(self.keys())
            key.remove('time')

            # Number of variables check:
            if len(var) > len(key):
                print('Not enough variables in IMF object:')
                print('\t%i listed, %i actual.\n' % (len(var),len(key)))
                return False
            # Each variable corresponds to only one in the dict
            # and occurs only once:
            for v in var:
                if v not in key:
                    print('Variable %s listed but does not exist.\n' % v)
                    return False
                if var.count(v) != 1:
                    print('Variable %s listed multiple times.\n' % v)
                    return False
            # Success!
            return True

        def read(self, infile):
            '''
            Read an SWMF IMF/solar wind input file into a newly
            instantiated imfinput object.
            '''
            import datetime as dt

            in_header = True
            with open(infile, 'r') as f:
                while True:
                    line = f.readline()
                    # All non-blank lines before first Param are header
                    if in_header:
                        if line.strip() and line[0] != '#':
                            self.attrs['header'].append(line)
                            continue
                        else:
                            in_header = False
                    # Parse all params.
                    # Grab line, continue if it's not a Param.
                    param = line.strip()
                    if not param or param[0] != '#':
                        continue
                    # For all possible Params, set object attributes/info.
                    if param[:5] == '#COOR':
                        self.attrs['coor'] = f.readline()[0:3]
                    elif param[:7] == '#REREAD':
                        self.attrs['reread']=True
                    elif param[:7] == '#ZEROBX':
                        setting = f.readline()[0]
                        self.attrs['zerobx'] = (setting == 'T')
                    elif param[:4] == '#VAR':
                        self.attrs['var'] = f.readline().split()
                        self.attrs['std_var']=False
                    elif param[:6] == '#PLANE':
                        xp = float(f.readline().split()[0])
                        yp = float(f.readline().split()[0])
                        self.attrs['plane']=[xp, yp]
                    elif param[:9] == '#POSITION':
                        yp = float(f.readline().split()[0])
                        zp = float(f.readline().split()[0])
                        self.attrs['satxyz'][1:]=(yp, zp)
                    elif param[:13] == '#SATELLITEXYZ':
                        xp = float(f.readline().split()[0])
                        yp = float(f.readline().split()[0])
                        zp = float(f.readline().split()[0])
                        self.attrs['satxyz']=[xp, yp, zp]
                    elif param[:10] == '#TIMEDELAY':
                        self.attrs['delay']=float(f.readline().split()[0])
                    elif param[:6] == '#START':
                        break
                    else:
                        raise Exception('Unknown file parameter: ' + param)

                # Read data
                indata = np.fromfile(f, sep=' ').reshape(
                    -1, 7 + len(self.attrs['var']))
            npoints = indata.shape[0]
            # Create containers for data.
            self['time'] = dmarray(np.empty(npoints, dtype=object))
            for key in self.attrs['var']:
                self[key] = dmarray(np.empty(npoints, dtype=np.float64))
            indata[:, 6] *= 1000 # to microseconds
            self['time'][:] = np.frompyfunc(dt.datetime, 7, 1)(
                *np.require(indata[:, 0:7], dtype=int).transpose())
            for i, name in enumerate(self.attrs['var']):
                self[name][:] = indata[:, i + 7]

        def write(self, outfile=False):
            '''
            Write the :class:`ImfInput` object to file.  Kwarg *outfile* can be
            used to specify the path of the output file; if it is not set,
            *self.attrs['file']* is used.  If this is not set, default to
            "imfinput.dat".
            '''

            import datetime as dt

            # Check that the var attribute agrees with the data dictionary.
            if not self.varcheck():
                raise Exception('Number of variables does not match variable order.')

            if not outfile:
                if self.attrs['file']!=None:
                    outfile=self.attrs['file']
                else:
                    outfile='imfinput.dat'

            with open(outfile, 'wb') as out:

                # Convenience variable:
                var=self.attrs['var']

                # Write the header:
                out.write('File created on {}\n'.format(dt.datetime.now().isoformat())
                          .encode())
                for head in self.attrs['header']:
                    out.write(head.encode())

                # Handle Params:
                if self.attrs['coor']:
                    out.write('#COOR\n{}\n\n'.format(self.attrs['coor']).encode())
                if self.attrs['zerobx']:
                    out.write(b'#ZEROBX\nT\n\n')
                if self.attrs['reread']:
                    out.write(b'#REREAD')
                if not self.attrs['std_var']:
                    out.write('#VAR\n{}\n\n'.format(' '.join(var)).encode())
                if self.attrs['satxyz'].count(None)<3:
                    xyz = self.attrs['satxyz']
                    if (xyz[0]==None) and (None not in xyz[1:]):
                        out.write('#POSITION\n{0[1]:-6.2f}\n{0[2]:-6.2f}\n\n'
                                  .format(xyz).encode())
                    elif None not in xyz:
                        out.write('#SATELLITEXYZ\n{}\n'.format(
                            ''.join("{:-6.2f}\n".format(n) for n in xyz)).encode())
                if self.attrs['delay']:
                    out.write('#DELAY\n{:-9.2f}\n\n'.format(self.attrs['delay'])
                              .encode())
                if None not in self.attrs['plane']:
                    out.write('#PLANE\n{}\n'.format(
                        ''.join('{:-6.2f}\n'.format(n)
                                for n in self.attrs['plane'])).encode())

                # Write the data:
                out.write(b'\n#START\n')
                # Round time to millisecond and format it
                timestr = np.vectorize(
                    lambda t: (t.replace(microsecond=0)
                    + dt.timedelta(microseconds=int(round(t.microsecond, -3))))
                    .strftime('%Y %m %d %H %M %S %f')[:-3] + ' ',
                    otypes=[bytes])(self['time'])
                outarray = np.column_stack([timestr] + [
                        np.char.mod('%10.2f', self[key]) for key in var])
                np.savetxt(out, outarray, delimiter=' ', fmt='%s')

        def add_pram_bz(self, target=None, loc=111, pcol='#CC3300', bcol='#3333CC',
                        xlim=None, plim=None, blim=None, epoch=None):
            '''
            Plot, on a single set of axes, both ram pressure and IMF Bz for a
            quick-look summary of an event.

            The figure and main axes object are returned.

            Parameters
            ==========

            Other Parameters
            ================
            target : Figure or Axes
                If None (default), a new figure is generated from scratch.
                If a matplotlib Figure object, a new axis is created
                to fill that figure.
                If a matplotlib Axes object, the plot is placed
                into that axis.
            loc : int
                Use to specify the subplot placement of the axis
                (e.g. loc=212, etc.) Used if target is a Figure or None.
                Default 111 (single plot).
            pcol : matplotlib color specifier
                Set the line and label color for ram pressure.
            bcol : matplotlib color specifier
                Set the line and label color for IMF Bz.
            xlim : 2-element list of datetimes
                Set the time range for the plot.  Defaults to full range.
            plim : 2-element list
                Set the y-axis range for dynamic pressure.
            blim : 2-element list
                Set the y-axis range for IMF Bz.
            epoch : datetime.datetime
                Create an epoch marker (vertical line) at time=epoch.

            Returns
            =======
            fig : matplotlib figure object
            ax  : matplotlib axes object
            '''

            import matplotlib.pyplot as plt
            from spacepy.plot import set_target, applySmartTimeTicks

            # Set ax and fig based on given target.
            fig, a1 = set_target(target, figsize=(8,4), loc=loc)
            a2 = a1.twinx()

            self.calc_pram()

            # Plot lines:
            a1.plot(self['time'], self['bz'],   color=bcol)
            a2.plot(self['time'], self['pram'], color=pcol, lw=1.5)

            # Restrict x-range:
            if not xlim: xlim=self['time']
            applySmartTimeTicks(a1, xlim, dolabel=True)

            # Zero line for IMF Bz:
            a1.hlines(0, xlim[0], xlim[-1], color=bcol, linestyles='dashed')

            # Label axes:
            a1.set_ylabel('IMF B$_{Z}$ ($nT$)', color=bcol, size=16)
            a2.set_ylabel('P$_{dyn}$ ($nPa$)',  color=pcol, size=16)
            plt.setp(a1.get_yticklabels(), color=bcol)
            plt.setp(a2.get_yticklabels(), color=pcol)

            # Add epoch marker.
            if epoch:
                ymin, ymax = a1.get_ylim()
                a1.vlines(epoch, ymin, ymax, linestyles='solid', color='g',
                          linewidths=2.)
                a1.set_ylim([ymin, ymax])

            return fig, a1

        def quicklook(self, timerange=None):
            '''
            Generate a quick-look plot of solar wind conditions driving the
            SWMF.  Default values show IMF, number density, and Earthward velocity.
            Returns a figure object containing the plots.
            '''

            import matplotlib.pyplot as plt
            from spacepy.plot import applySmartTimeTicks

            if not timerange:
                timerange = [self['time'][0], self['time'][-1]]

            def adjust_plots(ax, ylab, xlab=False, Zero=True):
                ax.grid(True)
                applySmartTimeTicks(ax,timerange)
                ax.set_ylabel(ylab)
                labels =ax.get_yticklabels()
                labels[-1].set_visible(False)
                labels[0].set_visible(False)
                if Zero:
                    ax.plot(timerange, [0,0], 'k--')
                if xlab:
                    ax.set_xlabel('Universal Time from %s' %
                                  timerange[0].isoformat())
                else:
                    ax.xaxis.set_ticklabels([])

            fig = plt.figure(figsize=(8,10))
            fig.subplots_adjust(hspace=0.025, top=0.95, bottom=0.05, right=0.95)

            a1 = fig.add_subplot(511)
            a1.plot(self['time'], self['bx'], lw=1.25, c='#003366')
            adjust_plots(a1, 'IMF $B_{X}$ ($nT$)')
            a1.set_title('Solar Wind Drivers (%s Coordinates)'
                         % (self.attrs['coor']))

            a2 = fig.add_subplot(512)
            a2.plot(self['time'], self['by'], lw=1.25, c='#333399')
            adjust_plots(a2, 'IMF $B_{Y}$ ($nT$)')

            a3 = fig.add_subplot(513)
            a3.plot(self['time'], self['bz'], lw=1.25, c='#0033CC')
            adjust_plots(a3, 'IMF $B_{Z}$ ($nT$)')

            a4 = fig.add_subplot(514)
            a4.plot(self['time'], self[self._denvar], lw=1.25, c='red')
            adjust_plots(a4, 'Density ($cm^{-3}$)', Zero=False)

            a5 = fig.add_subplot(515)
            a5.plot(self['time'], -1.0*self['ux'], lw=1.25, c='green')
            adjust_plots(a5, '$|V_{X}|$ ($km/s$)', Zero=False, xlab=True)

            return fig

    class SatOrbit(PbData):
        '''
        An class to load, read, write, and handle BATS-R-US satellite orbit
        input files.  These files are used to fly virtual satellites through
        the MHD domain.  Note that the output files should be handled by
        the :class:`LogFile` and not this satorbit object.  The object's
        required and always present attributes are:

        ============ ==============================================================
        Attribute    Description
        ============ ==============================================================
        head         A list of header lines for the file that contain comments.
        coor         The three-letter code (see SWMF doc) of the coord system.
        file         Location of the file to read/write.
        ============ ==============================================================

        The object should always have the following two data keys:

        ============ ==============================================================
        Key          Description
        ============ ==============================================================
        time         A list or numpy vector of datetime objects
        xyz          A 3 x len(time) numpy array of x,y,z coordinates associated
                     with the time vector.
        ============ ==============================================================

        A "blank" instantiation will create an empty object for the user to fill.
        This is desired if the user wants to create a new orbit, either from
        data or from scratch, and save it in a properly formatted file.  Here's
        an example with a "stationary probe" type orbit where the attributes are
        filled and then dropped to file:

        >>> from spacepy.pybats import SatOrbit
        >>> import datetime as dt
        >>> import numpy as np
        >>> sat = SatOrbit()
        >>> sat['time'] = [ dt.datetime(2000,1,1), dt.datetime(2000,1,2) ]
        >>> pos = np.zeros( (3,2) )
        >>> pos[:,0]=[6.6, 0, 0]
        >>> pos[:,1]=[6.6, 0, 0]
        >>> sat['xyz'] = pos
        >>> sat.attrs['coor'] = 'SMG'
        >>> sat.attrs['file'] = 'noon_probe.dat'
        >>> sat.write()

        If instantiated with a file name, the name is loaded into the object.
        For example,

        >>> sat=SatOrbit('a_sat_orbit_file.dat')

        ...will populate all fields with the data in *a_sat_orbit_file.dat*.
        '''

        def __init__(self, filename=None, *args, **kwargs):
            '''
            Create a satorbit object.  If I{file} is not given, buid an
            empty satorbit object is instantiated for the user to fill
            with values of their own creation.
            '''
            import numpy as np

            super(SatOrbit,self).__init__(*args, **kwargs)

            self.attrs['file'] = filename
            self.attrs['head']=[]
            self.attrs['coor'] = 'GSM'
            self['time'] = dmarray(np.zeros(0, dtype=object))

            if filename:
                try:
                    self.read()
                except IOError as reason:
                    self['xyz'] = dmarray(np.zeros( (3,1) ))
                    raise IOError(reason)
            else:
                # fill with empty stuff.
                self['xyz'] = dmarray(np.zeros( (3,1) ))

        def read(self):
            '''
            Read and parse a satellite orbit input file into the satorbit object.
            '''
            import numpy as np
            import datetime as dt
            # Try to open the file.  If we fail, pass the exception
            # to the caller.
            infile = open(self.attrs['file'], 'r')

            # Slurp contents
            raw = infile.readlines()
            infile.close()

            # Parse header.  Save coordinate system and any comments.
            while 1:
                line = raw.pop(0).strip()
                if line:
                    if line == '#START':
                        break
                    elif line == '#COOR':
                        self.attrs['coor'] = raw.pop(0).strip()
                    else:
                        self.attrs['head'].append(line)

            # Read and store all values.
            npts = len(raw)
            self['xyz'] = dmarray(np.zeros( (3,npts) ))
            self['time']=dmarray(np.zeros(npts, dtype=object))
            for i, line in enumerate(raw):
                parts = line.split()
                self['time'][i]=dt.datetime(
                        int(parts[0]), #year
                        int(parts[1]), #month
                        int(parts[2]), #day
                        int(parts[3]), #hour
                        int(parts[4]), #min
                        int(parts[5]), #sec
                        int(parts[6]) * 1000 #micro seconds
                        )
                self['xyz'][:,i] = parts[7:]

        def write(self):
            '''Write a L{satorbit object<pybats.satorbit>} to file using the
            correct SWMF-input format.  The file will be written to the
            location specified by self.filename.  An error is thrown if this
            attribute is not set.
            '''

            try:
                outfile = open(self.attrs['file'], 'w')
            except:
                raise Exception('Could not open self.filename!')

            # Start by writing header, coordinate system, and then #START.
            for line in self.attrs['head']:
                outfile.write(line+'\n')
            outfile.write('\n')
            outfile.write('#COOR\n{}\n\n'.format(self.attrs['coor']))
            outfile.write('#START\n')

            # Write the rest of the orbit.
            npts=len(self['time'])
            for i in range(npts):
                #Time:
                outfile.write('{:%Y %m %d %H %M %S} {:03.0f} '.format(
                    self['time'][i], self['time'][i].microsecond/1000.0) )
                #Position:
                outfile.write('{:13.7E} {:13.7E} {:13.7E}\n'.format(
                    self['xyz'][0,i], self['xyz'][1,i], self['xyz'][2,i]) )

            outfile.close()