Python datamodel.dmarray函数代码示例

def FIRE_HiRes_L1_L2(datafile, ephemfile):
    full_data = dm.readJSONheadedASCII(datafile)
    ephem = dm.readJSONheadedASCII(ephemfile)
    data = Trim_data_file(full_data, ephem)
    labels = ephem.keys()
    ephem_fields = ['Lsimple', 'CDMAG_MLT']
    dt = spt.Ticktock(data['Epoch']).TAI
    et = spt.Ticktock(ephem['DateTime']).TAI
    for i in range(len(ephem_fields)):
        print ephem_fields[i]
        y = ephem[ephem_fields[i]]
        nx = tb.interpol(dt, et, y)
        data[ephem_fields[i]] = dm.dmarray(nx)
    ephem_lat = ephem['Rgeod_LatLon'][:,0]
    ephem_lon = ephem['Rgeod_LatLon'][:,1]
    nx = tb.interpol(dt, et, ephem_lat)
    data['Lat'] = dm.dmarray(nx)
    nx = tb.interpol(dt, et, ephem_lon)
    data['Lon'] = dm.dmarray(nx)
    n_lines = len(data['Epoch'])
    eflux = np.zeros(n_lines,12)
    day = ephem['DateTime'][0][0:10]
    outfile = datafile[:-23] + day + '-HiRes_L2.txt'
    dm.toJSONheadedASCII(outfile, data)
    return data

    def test_defaults(self):
        """run it and check that defaults were set correctly"""
        a = spectrogram(self.data, variables=self.kwargs['variables'])
        ans = {'bins': [dm.dmarray([ 730120.0,  730135.30769231,  730150.61538462,
        730165.92307692,  730181.23076923,  730196.53846154,
        730211.84615385,  730227.15384615,  730242.46153846,
        730257.76923077,  730273.07692308,  730288.38461538,
        730303.69230769,  730319.        ]),
                   dm.dmarray([ 0.00169679,  0.07848775,  0.1552787 ,  0.23206965,  0.30886061,
                               0.38565156,  0.46244251,  0.53923347,  0.61602442,  0.69281538,
                               0.76960633,  0.84639728,  0.92318824,  0.99997919])],
                'variables': ['xval', 'yval', 'zval'],
                'ylim': (0.0012085702179961411, 0.99323954710300699),
                'zlim': (0.001696792515639145, 0.99997919064162388)}
        for key in ans:
            if key == 'variables':
                self.assertEqual(a.specSettings[key], ans[key])
            else:
                if key == 'bins':
#                    np.testing.assert_allclose(a.specSettings[key], ans[key], atol=1e-2, rtol=1e-3)
                    np.testing.assert_almost_equal(a.specSettings[key], ans[key], decimal=2)
                else:
#                    np.testing.assert_allclose(a.specSettings[key], ans[key], rtol=1e-5)
                    np.testing.assert_almost_equal(a.specSettings[key], ans[key], decimal=6)
        self.assertRaises(NotImplementedError, a.add_data, self.data)

    def _parse(self, lines):
        '''
        Given raw ascii input as a list of lines, parse into object.
        '''

        if lines[0][0:7]==' Format': #IAGA-2002 formatted file.
            data=parse_iaga(lines, iagacode='DST')
            for key in data:
                self[key]=data[key]
            self['dst'].attrs['units']='nT'
            self.attrs['npts']=data['time'].size
            return
    
        self.attrs['npts']=len(lines)
        time = []
        dst  = np.zeros(24*self.attrs['npts'])
        for i,line in enumerate(lines):
            # Get year, month, day.
            try:
                yy = int(line[14:16]) * 100
            except:
                yy = 1900
            yy = yy + int(line[3:5])
            dd = int(line[8:10])
            mm = int(line[5:7 ])
        
        # Parse the rest of the data.
            for j in range(0,24):
                time.append(dt.datetime(yy, mm, dd, j))
                loc = 20 + 4*j
                dst[24*i + j] = float(line[loc:loc+4])

        self['time']= dmarray(time)
        self['dst'] = dmarray(dst, attrs={'units':'nT'})

    def __init__(self, inlst):
        dt = zip(*inlst)[0]
        data = np.hstack(zip(*inlst)[1]).reshape((-1, 1))
        dat = dm.SpaceData()

        dat['time'] = dm.dmarray(data[:,0])
        dat['time'].attrs['CATDESC'] = 'Start or stop time'
        dat['time'].attrs['FIELDNAM'] = 'time'
        dat['time'].attrs['LABLAXIS'] = 'Start or stop time'
        dat['time'].attrs['SCALETYP'] = 'linear'
        #dat['time'].attrs['UNITS'] = 'none'
        dat['time'].attrs['UNITS'] = 'ms'
        dat['time'].attrs['VALIDMIN'] = datetime.datetime(1990,1,1)
        dat['time'].attrs['VALIDMAX'] = datetime.datetime(2029,12,31,23,59,59,999000)
        dat['time'].attrs['VAR_TYPE'] = 'support_data'
        dat['time'].attrs['VAR_NOTES'] = 'Time data started or stopped'
        dat['time'].attrs['DEPEND_0'] = 'Epoch'
        dat['time'].attrs['FILLVAL'] = 'None'

        dat['Epoch'] = dm.dmarray(dt)
        dat['Epoch'].attrs['CATDESC'] = 'Default Time'
        dat['Epoch'].attrs['FIELDNAM'] = 'Epoch'
        #dat['Epoch'].attrs['FILLVAL'] = datetime.datetime(2100,12,31,23,59,59,999000)
        dat['Epoch'].attrs['LABLAXIS'] = 'Epoch'
        dat['Epoch'].attrs['SCALETYP'] = 'linear'
        dat['Epoch'].attrs['UNITS'] = 'ms'
        dat['Epoch'].attrs['VALIDMIN'] = datetime.datetime(1990,1,1)
        dat['Epoch'].attrs['VALIDMAX'] = datetime.datetime(2029,12,31,23,59,59,999000)
        dat['Epoch'].attrs['VAR_TYPE'] = 'support_data'
        dat['Epoch'].attrs['TIME_BASE'] = '0 AD'
        dat['Epoch'].attrs['MONOTON'] = 'INCREASE'
        dat['Epoch'].attrs['VAR_NOTES'] = 'Epoch at each configuration point'

        self.data = dat

 def test_resample1(self):
     '''resample should give consistent results'''
     ans = dm.SpaceData()
     ans.attrs['foo'] = 'bar'
     ans['a'] = [ 1.,  3.,  5.,  7.]
     ans['b'] = dm.dmarray([5.,   7.,   9.,  11.])
     ans['b'].attrs['marco'] = 'polo'
     ans['Epoch'] = [datetime.datetime(2010, 1, 1, 1, 0),
                     datetime.datetime(2010, 1, 1, 3, 0),
                     datetime.datetime(2010, 1, 1, 5, 0),
                     datetime.datetime(2010, 1, 1, 7, 0)]
     
     
     a = dm.SpaceData()
     a['a'] = dm.dmarray(range(10))
     a['b'] = dm.dmarray(range(10)) + 4
     a['b'].attrs['marco'] = 'polo'
     a['c'] = dm.dmarray(range(3)) + 10
     a.attrs['foo'] = 'bar'
     times = [datetime.datetime(2010, 1, 1) + datetime.timedelta(hours=i) for i in range(10)]
     out = dm.resample(a, times, winsize=datetime.timedelta(hours=2), overlap=datetime.timedelta(hours=0))
     for k, v in out.items():
         np.testing.assert_equal(v,  ans[k])
     self.assertEqual(ans.attrs, out.attrs)
     self.assertEqual(ans['b'].attrs['marco'], 'polo')
     self.assertTrue(out['b'].attrs['DEPEND_0'], 'Epoch')
     self.assertFalse('c' in out)

    def __init__(self,filename,*args,**kwargs):
        '''
        Reads the data; sorts into arrays.
        '''

        import datetime as dt
        from spacepy.datamodel import dmarray
        from matplotlib.dates import date2num

        super(MltSlice, self).__init__(*args, **kwargs)  # Init as PbData.
        self.attrs['file'] = filename

        f = open(filename, 'r')
    
        # Parse header.
        self.attrs['mlt'] = float(f.readline().split()[-1])
        self['L'] = dmarray(np.array(f.readline().split()[1:], dtype=float),
                            {'units':'$R_E$'})

        # Parse remainder of file.
        lines = f.readlines()
        self['n']    = dmarray(np.zeros([len(lines), len(self['L'])]), 
                               {'units':'cm^{-3}'})
        self['time'] = dmarray(np.zeros(len(lines), dtype=object))

        for i,l in enumerate(lines):
            p = l.split()
            self['time'][i] = dt.datetime(int(p[0]), int(p[1]), int(p[2]), 
                                          int(p[3]), int(p[4]), int(p[5]),
                                          int(p[6])*1000)
            self['n'][i,:] = p[7:]

        # Some "hidden" variables for plotting.
        self._dtime = date2num(self['time'])
        self._dy = self['L'][1] - self['L'][0]

 def setUp(self):
     super(spectrogramTests, self).setUp()
     self.kwargs = {}
     self.kwargs['variables'] = ['xval', 'yval', 'zval']
     np.random.seed(8675309)
     self.data = dm.SpaceData(xval = dm.dmarray(np.random.random_sample(200)), 
                         yval = dm.dmarray(np.random.random_sample(200)), 
                         zval = dm.dmarray(np.random.random_sample(200)))

 def test_resample_shape(self):
     '''resample should give consistent results, 1d or 2d'''
     a = dm.SpaceData()
     a['a'] = dm.dmarray(range(10*3*4)).reshape(10,3,4)
     a['b'] = dm.dmarray(range(10)) + 4
     a['c'] = dm.dmarray(range(3)) + 10
     times = [datetime.datetime(2010, 1, 1) + datetime.timedelta(hours=i) for i in range(10)]
     self.assertRaises(IndexError, dm.resample, a, times, datetime.timedelta(hours=2), datetime.timedelta(hours=0))

    def __init__(self, inlst):
        if not inlst:
            print("**  No packets decoded cannot continue  **")
            sys.exit(1)
        dt = zip(*inlst)[0]
        data = np.hstack(zip(*inlst)[1]).reshape((-1, 2))

        # go through Context and change the data type and set the None to fill
        tmp = np.zeros(data.shape, dtype=int)
        for (i, j), val in np.ndenumerate(data):
            try:
                tmp[i,j] = val
            except (TypeError, ValueError):
                tmp[i,j] = -2**16-1


        dat = dm.SpaceData()

        dat['Context'] = dm.dmarray(tmp[:])
        dat['Context'].attrs['CATDESC'] = 'Context data'
        dat['Context'].attrs['FIELDNAM'] = 'Context'
        dat['Context'].attrs['ELEMENT_LABELS'] = "Det_0", "Det_1",
        dat['Context'].attrs['ELEMENT_NAMES'] = "Det_0", "Det_1",
        dat['Context'].attrs['LABEL'] = 'Context data'
        dat['Context'].attrs['SCALE_TYPE'] = 'log'
        #dat['time'].attrs['UNITS'] = 'none'
        dat['Context'].attrs['UNITS'] = ''
        dat['Context'].attrs['VALID_MIN'] = 0
        dat['Context'].attrs['VALID_MAX'] = 2**15-1
        dat['Context'].attrs['VAR_TYPE'] = 'data'
        dat['Context'].attrs['VAR_NOTES'] = 'Context data 6s average'
        dat['Context'].attrs['DEPEND_0'] = 'Epoch'
        dat['Context'].attrs['FILL_VALUE'] = -2**16-1

        dat['Epoch'] = dm.dmarray(dt)
        dat['Epoch'].attrs['CATDESC'] = 'Default Time'
        dat['Epoch'].attrs['FIELDNAM'] = 'Epoch'
        #dat['Epoch'].attrs['FILLVAL'] = datetime.datetime(2100,12,31,23,59,59,999000)
        dat['Epoch'].attrs['LABEL'] = 'Epoch'
        dat['Epoch'].attrs['SCALE_TYPE'] = 'linear'
        #        dat['Epoch'].attrs['VALID_MIN'] = datetime.datetime(1990,1,1)
        #        dat['Epoch'].attrs['VALID_MAX'] = datetime.datetime(2029,12,31,23,59,59,999000)
        dat['Epoch'].attrs['VAR_TYPE'] = 'support_data'
        dat['Epoch'].attrs['TIME_BASE'] = '0 AD'
        dat['Epoch'].attrs['MONOTON'] = 'INCREASE'
        dat['Epoch'].attrs['VAR_NOTES'] = 'Epoch at each configuration point'


        # go through and remove duplicate times and data
        print("Looking for duplicate measurements")

        arr, dt_ind, return_inverse = np.unique(dat['Epoch'], return_index=True, return_inverse=True) # this is unique an sort
        print("Found {0} duplicates of {1}".format(len(return_inverse)-len(dt_ind), len(return_inverse)))

        dat['Epoch'] = arr
        dat['Context'] = dat['Context'][dt_ind]

        self.data = dat

    def __init__(self, inlst):
        dt = zip(*inlst)[0]
        data = np.hstack(zip(*inlst)[1]).reshape((-1, 1))
        dat = dm.SpaceData()

        dat['Time'] = dm.dmarray(data[:,0])
        dat['Time'].attrs['CATDESC'] = 'Start or stop Time'
        dat['Time'].attrs['FIELDNAM'] = 'Time'
        dat['Time'].attrs['LABLAXIS'] = 'Start or stop Time'
        dat['Time'].attrs['SCALETYP'] = 'linear'
        #dat['Time'].attrs['UNITS'] = 'none'
        dat['Time'].attrs['VALIDMIN'] = datetime.datetime(1990,1,1)
        dat['Time'].attrs['VALIDMAX'] = datetime.datetime(2029,12,31,23,59,59,999000)
        dat['Time'].attrs['VAR_TYPE'] = 'support_data'
        dat['Time'].attrs['VAR_NOTES'] = 'Time data started or stopped'
        dat['Time'].attrs['DEPEND_0'] = 'Epoch'
        dat['Time'].attrs['FILLVAL'] = 'None'

        dat['Epoch'] = dm.dmarray(dt)
        dat['Epoch'].attrs['CATDESC'] = 'Default Time'
        dat['Epoch'].attrs['FIELDNAM'] = 'Epoch'
        #dat['Epoch'].attrs['FILLVAL'] = datetime.datetime(2100,12,31,23,59,59,999000)
        dat['Epoch'].attrs['LABLAXIS'] = 'Epoch'
        dat['Epoch'].attrs['SCALETYP'] = 'linear'
        dat['Epoch'].attrs['VALIDMIN'] = datetime.datetime(1990,1,1)
        dat['Epoch'].attrs['VALIDMAX'] = datetime.datetime(2029,12,31,23,59,59,999000)
        dat['Epoch'].attrs['VAR_TYPE'] = 'support_data'
        dat['Epoch'].attrs['TIME_BASE'] = '0 AD'
        dat['Epoch'].attrs['MONOTON'] = 'INCREASE'
        dat['Epoch'].attrs['VAR_NOTES'] = 'Epoch at each configuration point'

        dat['Mode'] = dm.dmarray(np.zeros(len(dt), dtype=int))
        dat['Mode'][...] = -1
        dat['Mode'].attrs['FIELDNAM'] = 'Mode'
        dat['Mode'].attrs['FILLVAL'] = -1
        dat['Mode'].attrs['LABLAXIS'] = 'FIRE Mode'
        dat['Mode'].attrs['SCALETYP'] = 'linear'
        dat['Mode'].attrs['VALIDMIN'] = 0
        dat['Mode'].attrs['VALIDMAX'] = 1
        dat['Mode'].attrs['VAR_TYPE'] = 'support_data'
        dat['Mode'].attrs['VAR_NOTES'] = 'Is the line FIRE on (=1) or FIRE off (=0)'
        dat['Mode'][::2] = 1
        dat['Mode'][1::2] = 0

        dat['Duration'] = dm.dmarray(np.zeros(len(dt), dtype=int))
        dat['Duration'][...] = -1
        dat['Duration'].attrs['FIELDNAM'] = 'Duration'
        dat['Duration'].attrs['FILLVAL'] = -1
        dat['Duration'].attrs['LABLAXIS'] = 'FIRE Duration'
        dat['Duration'].attrs['SCALETYP'] = 'linear'
        dat['Duration'].attrs['VALIDMIN'] = 0
        dat['Duration'].attrs['VALIDMAX'] = 100000
        dat['Duration'].attrs['VAR_TYPE'] = 'support_data'
        dat['Duration'].attrs['VAR_NOTES'] = 'Duration of the on or off'
        df = np.asarray([ v1 - v2 for v1, v2 in zip(dat['Time'],dat['Epoch']) ])
        dat['Duration'][...] = np.asarray([ v.days*24*60*60 + v.seconds for v in df ])
        
        self.data = dat

 def test_toRecArray(self):
     '''a record array can be created from a SpaceData'''
     sd = dm.SpaceData()
     sd['x'] = dm.dmarray([1.0, 2.0])
     sd['y'] = dm.dmarray([2,4])
     ra = dm.toRecArray(sd)
     np.testing.assert_equal(ra['x'], [1.0, 2.0])
     np.testing.assert_equal(ra['y'], [2, 4])
     self.assertEqual(ra.dtype, np.dtype((np.record, [('x', '<f8'), ('y', '<i8'), ])))

 def test_HDF5roundtrip2GZIP(self):
     """Data can go to hdf without altering datetimes in the datamodel with compression"""
     a = dm.SpaceData()
     a['foo'] = dm.SpaceData()
     dm.toHDF5(self.testfile, a, compression='gzip')
     newobj = dm.fromHDF5(self.testfile)
     self.assertEqual(a['foo'], newobj['foo'])
     a['bar'] = dm.dmarray([datetime.datetime(2000, 1, 1)])
     dm.toHDF5(self.testfile, a, compression='gzip')
     self.assertEqual(a['bar'], dm.dmarray([datetime.datetime(2000, 1, 1)]))

    def calc_deg(self):
        """
        Gitm defaults to radians for lat and lon, which is sometimes difficult
        to use.  This method creates *dLat* and *dLon*, which is lat and lon
        in degrees.
        """
        from numpy import pi

        if "Latitude" in self:
            self["dLat"] = dmarray(self["Latitude"] * 180.0 / pi, attrs={"units": "degrees"})
        if "Longitude" in self:
            self["dLon"] = dmarray(self["Longitude"] * 180.0 / pi, attrs={"units": "degrees"})

 def test_creation_dmarray(self):
     """When a dmarray is created it should have attrs empty or not"""
     self.assertTrue(hasattr(self.dat, 'attrs'))
     self.assertEqual(self.dat.attrs['a'], 'a')
     data = dm.dmarray([1,2,3])
     self.assertTrue(hasattr(data, 'attrs'))
     self.assertEqual(data.attrs, {})
     data2 = dm.dmarray([1,2,3], attrs={'coord':'GSM'})
     self.assertEqual(data.attrs, {})
     self.assertEqual(data2.attrs, {'coord':'GSM'})
     data2 = dm.dmarray([1,2,3], dtype=float, attrs={'coord':'GSM'})
     np.testing.assert_almost_equal([1,2,3], data2)

def GSMtoMLT(gsm, dt):
    """
    convert GSM values to MLT in the lgm way

    Parameters
    ----------
    gsm : array_like
        Nx3 array_like of the GSM position
    dt : array_like
        N elementarray_like of datetime objects

    Returns
    -------
    out : numpy.array
        N element array of the MLT values
    """
    def doConv(gsm, dt):
        Pgsm = Lgm_Vector.Lgm_Vector(*gsm)
        Pwgs = Lgm_Vector.Lgm_Vector()
        Pmlt = Lgm_Vector.Lgm_Vector()
        cT = pointer(Lgm_CTrans())
        Lgm_Set_Coord_Transforms( dateToDateLong(dt), dateToFPHours(dt), cT)

        Lgm_Convert_Coords( pointer(Pgsm), pointer(Pwgs), GSM_TO_WGS84, cT )
        Lgm_Convert_Coords( pointer(Pwgs), pointer(Pmlt), WGS84_TO_EDMAG, cT )
        R, MLat, MLon, MLT = c_double(), c_double(), c_double(), c_double(),
        Lgm_EDMAG_to_R_MLAT_MLON_MLT( pointer(Pmlt),  pointer(R), pointer(MLat), pointer(MLon),
            pointer(MLT), cT)
        return MLT.value

    gsm_ = numpy.asanyarray(gsm)
    if isinstance(dt, datetime.datetime):
        dt_ = numpy.asanyarray([dt])
    else:
        dt_ = numpy.asanyarray(dt)
    if gsm_.ndim == 2:
        if gsm_.shape[1] != 3:
            raise(ValueError("Invalid vector shape"))
        if gsm_.shape[0] != dt_.size:
            if dt_.size == 1:
                dt_ = dm.dmarray([dt_]*gsm_.shape[0])
            else:
                raise(ValueError("Array size mismatch"))
        ans = dm.dmarray(numpy.empty(len(dt_)), dtype=numpy.double, attrs={'coord_system': 'EDMAG'})
        for ii, (gsm_val, dt_val) in enumerate(itertools.izip(gsm_, dt_)):
            ans[ii] = doConv(gsm_val, dt_val)
    else:
        if dt_.size==1:
            ans = dm.dmarray([doConv(gsm_, dt_)], attrs={'coord_system': 'EDMAG'})
        else:
            ans = dm.dmarray(doConv(gsm_, dt_), attrs={'coord_system': 'EDMAG'})
    return ans