def invert_example(axes=[], show=True):
"""Test case for reverse engineering rough solar wind params
We look here at the 1989 storm as studied by Nagatsuma (2015)
and Boteler (2019)
"""
import datetime as dt
import spacepy.toolbox as tb
# fetch doesn't respect the days (it grabs 1 month at a time)
# but we'll give the days we're interested in for reference
st_tup = (1989, 3, 12)
en_tup = (1989, 3, 15)
ky_dat = kyo.fetch('dst', st_tup, en_tup)
st = dt.datetime(*st_tup)
en = dt.datetime(*en_tup)
inds = tb.tOverlapHalf([st, en], ky_dat['time'])
vbz = inverseBurton(ky_dat['dst'][inds], rectify=True)
vbz_OB = inverseOBrienMcPherron(ky_dat['dst'][inds])
if not axes:
fig, ax = plt.subplots(2, sharex=True, figsize=(10, 4.5))
else:
ax = axes
ax[0].plot(ky_dat['time'][inds], vbz / 1e3, 'r-', label='Burton')
ax[0].set_ylabel('v.B$_{south}$ [mV/m]')
ax[0].plot(ky_dat['time'][inds], vbz_OB / 1e3, 'b-', label='O-M')
ax[0].set_ylabel('v.B$_{south}$ [mV/m]')
ax[0].legend()
ax[1].plot(ky_dat['time'][inds], ky_dat['dst'][inds])
ax[1].set_ylabel('Dst [nT]')
ax[1].set_xlabel('1989')
if show:
plt.show()
def applyRefractory(process1, period):
'''Apply a refractory period to an input discrete event time sequence
All events in the refractory period are removed from the point process.
Parameters
==========
process1 : iterable
an iterable of datetimes, or a spacepy.time.Ticktock
period : datetime.timedelta
length of refractory period
Returns
=======
keep : iterable
returns pruned set of datetimes with same type as input
NOTE: array subclasses will be lost
'''
import spacepy.time as spt
if isinstance(process1, spt.Ticktock):
#SpacePy Ticktock
p1 = dm.dmcopy(process1.UTC).tolist()
tickt = True
elif isinstance(process1[0], dt.datetime):
#iterable containing datetimes
p1 = dm.dmcopy(process1)
try:
p1 = p1.tolist()
wasArr = True
except:
wasArr = False
tickt = False
else:
raise NotImplementedError(
'Input process must be a list/array of datetimes, or a spacepy.time.Ticktock'
)
try:
assert period.seconds
except AssertionError:
raise AttributeError('period must be a datetime.timedelta')
done = len(p1) < 2
keep, discard = [], []
while not done:
t1 = p1[0]
t2 = t1 + period
inds = tb.tOverlapHalf([t1, t2], p1[1:])
for idx in inds:
discard.append(p1.pop(idx + 1))
keep.append(p1.pop(0)) # put test element into keep array
done = len(p1) < 2
if tickt:
return spt.Ticktock(keep)
else:
if wasArr:
return np.array(keep)
else:
return keep
def applyRefractory(process1, period):
'''Apply a refractory period to an input discrete event time sequence
All events in the refractory period are removed from the point process.
Parameters
==========
process1 : iterable
an iterable of datetimes, or a spacepy.time.Ticktock
period : datetime.timedelta
length of refractory period
Returns
=======
keep : iterable
returns pruned set of datetimes with same type as input
NOTE: array subclasses will be lost
'''
import spacepy.time as spt
if isinstance(process1, spt.Ticktock):
#SpacePy Ticktock
p1 = dm.dmcopy(process1.UTC).tolist()
tickt = True
elif isinstance(process1[0], dt.datetime):
#iterable containing datetimes
p1 = dm.dmcopy(process1)
try:
p1 = p1.tolist()
wasArr = True
except:
wasArr = False
tickt = False
else:
raise NotImplementedError('Input process must be a list/array of datetimes, or a spacepy.time.Ticktock')
try:
assert period.seconds
except AssertionError:
raise AttributeError('period must be a datetime.timedelta')
done = len(p1)<2
keep, discard = [], []
while not done:
t1 = p1[0]
t2 = t1 + period
inds = tb.tOverlapHalf([t1, t2], p1[1:])
for idx in inds:
discard.append(p1.pop(idx+1))
keep.append(p1.pop(0)) # put test element into keep array
done = len(p1)<2
if tickt:
return spt.Ticktock(keep)
else:
if wasArr:
return np.array(keep)
else:
return keep
def test_tOverlapHalf_random(self):
"""Shuffle input before calling tOverlapHalf"""
real_ans = [1, 2, 6, 7, 10, 12, 13, 14, 15, 17, 18,
19, 24, 27, 28, 30, 32, 35, 37, 38]
random.seed(0)
#Cut down the python3 rng to the same precision as python2
random.shuffle(self.dt_a, lambda:round(random.random(), 9))
random.shuffle(self.dt_b, lambda:round(random.random(), 9))
ans = tb.tOverlapHalf(self.dt_a, self.dt_b)
self.assertEqual(real_ans, ans)
def test_tOverlapHalf_random(self):
"""Shuffle input before calling tOverlapHalf"""
real_ans = [1, 2, 6, 7, 10, 12, 13, 14, 15, 17, 18,
19, 24, 27, 28, 30, 32, 35, 37, 38]
random.seed(0)
#Cut down the python3 rng to the same precision as python2
random.shuffle(self.dt_a, lambda:round(random.random(), 9))
random.shuffle(self.dt_b, lambda:round(random.random(), 9))
ans = tb.tOverlapHalf(self.dt_a, self.dt_b)
self.assertEqual(real_ans, ans)
def initialSW():
"""Construct initial estimate of hourly solar wind parameters"""
st = dt.datetime(1989, 3, 12)
en = dt.datetime(1989, 3, 15)
hourly = getKondrashovSW()
# Keep IMF By and n, set V to Boteler/Nagatsuma
t_ssc1 = dt.datetime(1989, 3, 13, 1, 27)
t_ssc2 = dt.datetime(1989, 3, 13, 7, 43)
t_cme = dt.datetime(1989, 3, 13, 16)
t_turn = dt.datetime(1989, 3, 14, 2)
# Set Bx positive, in accordance with ISEE-3 data, Vy/Vz->0
hourly['Bx'] = dm.dmfilled(hourly['By'].shape, fillval=3)
hourly['Vy'] = dm.dmfilled(hourly['By'].shape, fillval=0)
hourly['Vz'] = dm.dmfilled(hourly['By'].shape, fillval=0)
# Before first SSC
inds_before_1 = tb.tOverlapHalf([dt.datetime(1989, 3, 12), t_ssc1],
hourly['DateTime'])
hourly['V_sw'][inds_before_1] = 400
# Between first and ssecond SSC
inds_between_12 = tb.tOverlapHalf([t_ssc1, t_ssc2], hourly['DateTime'])
hourly['V_sw'][inds_between_12] = 550
# IMF turns north around 1989-03-14T02:00:00 according to inverse Burton and Kondrashov
inds_mainphase = tb.tOverlapHalf([t_ssc2, t_turn], hourly['DateTime'])
hourly['V_sw'][inds_mainphase] = 983
# Then have speed decay towards IMP-8 measurement which is ballpark 820 km/s
inds_rest = tb.tOverlapHalf([t_turn, hourly['DateTime'][-1]],
hourly['DateTime'])
hourly['V_sw'][inds_rest] = np.linspace(983, 820, len(inds_rest))
# Now we have speed, estimate temperature
hourly['Plasma_temp'] = emp.getExpectedSWTemp(hourly['V_sw'],
model='XB15',
units='K')
inds_cme = tb.tOverlapHalf([t_cme, en], hourly['DateTime'])
hourly['Plasma_temp'][
inds_cme] /= 3 # reduce by factor of 3 for CME-like temp
# Get "Kondrashov VBs" using V from Boteler/Nagatsuma
hourly['VBs_K'] = 1e-3 * hourly['V_sw'] * rectify(
-1 * hourly['Bz']) # mV/m
# Now get VBs from inverse Burton
ky_dat = kyo.fetch('dst', (st.year, st.month, st.day),
(en.year, en.month, en.day))
inds = tb.tOverlapHalf([st, en], ky_dat['time'])
# Pressure correct here using n and V
hourly = calc_P(hourly)
hourly['Dst'] = ky_dat['dst'][inds]
dst_star = emp.getDststar(hourly, model='OBrien')
hourly['VBs_OB'] = 1e-3 * inverseOBrienMcPherron(dst_star)
# Make new Bz from VBs_OB
hourly['Bz_OB'] = -1e3 * hourly['VBs_OB'] / hourly['V_sw'] # nT
return hourly
def test_tOverlapHalfSorted(self):
"""Get overlap of only one list, exploiting the sort"""
real_ans = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
ans = tb.tOverlapHalf(self.dt_a, self.dt_b, presort=True)
numpy.testing.assert_array_equal(real_ans, ans)
def test_tOverlapHalf(self):
"""Get overlap of only one list"""
real_ans = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
ans = tb.tOverlapHalf(self.dt_a, self.dt_b)
self.assertEqual(real_ans, ans)
def get_omni(ticks, dbase='QDhourly', **kwargs):
'''
Returns Qin-Denton OMNI values, interpolated to any time-base from a default hourly resolution
The update function in toolbox retrieves all available hourly Qin-Denton data,
and this function accesses that and interpolates to the given times,
returning the OMNI values as a SpaceData (dict-like) with
Kp, Dst, dens, velo, Pdyn, ByIMF, BzIMF, G1, G2, G3, etc.
(see also http://www.dartmouth.edu/~rdenton/magpar/index.html and
http://www.agu.org/pubs/crossref/2007/2006SW000296.shtml )
Parameters
==========
ticks : Ticktock class or array-like of datetimes
time values for desired output
dbase : str (optional)
Select data source, options are 'QDhourly', 'OMNI2', 'Mergedhourly'
Note - Custom data sources can be specified in the spacepy config file
as described in the module documentation.
Returns
=======
out : spacepy.datamodel.SpaceData
containing all Qin-Denton values at times given by ticks
Examples
========
>>> import spacepy.time as spt
>>> import spacepy.omni as om
>>> ticks = spt.Ticktock(['2002-02-02T12:00:00', '2002-02-02T12:10:00'], 'ISO')
>>> d = om.get_omni(ticks)
>>> d.tree(levels=1)
+
|____ByIMF
|____Bz1
|____Bz2
|____Bz3
|____Bz4
|____Bz5
|____Bz6
|____BzIMF
|____DOY
|____Dst
|____G1
|____G2
|____G3
|____Hr
|____Kp
|____Pdyn
|____Qbits
|____RDT
|____UTC
|____W1
|____W2
|____W3
|____W4
|____W5
|____W6
|____Year
|____akp3
|____dens
|____ticks
|____velo
Notes
=====
Note about Qbits: If the status variable is 2, the quantity you are using is fairly well
determined. If it is 1, the value has some connection to measured values, but is not directly
measured. These values are still better than just using an average value, but not as good
as those with the status variable equal to 2. If the status variable is 0, the quantity is
based on average quantities, and the values listed are no better than an average value. The
lower the status variable, the less confident you should be in the value.
'''
dbase_options = {'QDhourly' : 1,
'OMNI2hourly' : 2,
'Mergedhourly': 3,
'Test' : -9,
}
if not isinstance(ticks, spt.Ticktock):
try:
ticks = spt.Ticktock(ticks, 'UTC')
except:
raise TypeError('get_omni: Input times must be a Ticktock object or a list of datetime objects')
if not dbase in dbase_options:
from spacepy import config
if dbase in config:
#If a dbase is specified that isn't a default, then it MUST be in the spacepy config
qdpath = os.path.split(os.path.split(config[dbase])[0])[0]
if not os.path.isdir(qdpath): raise IOError('Specified dbase ({0}) does not have a valid location ({1})'.format(dbase, config[dbase]))
days = list(set([tt.date() for tt in ticks.UTC]))
flist = ['']*len(days)
fnpath, fnformat = os.path.split(config[dbase])
for idx, day in enumerate(days):
dp = fnpath.replace('YYYY', '{0}'.format(day.year))
df = fnformat.replace('YYYY', '{0}'.format(day.year))
df = df.replace('MM', '{0:02d}'.format(day.month))
df = df.replace('DD', '{0:02d}'.format(day.day))
flist[idx] = os.path.join(dp, df)
if 'convert' in kwargs:
convdict = kwargs['convert']
else:
convdict = True #set to True as default?
if 'interp' not in kwargs:
kwargs['interp'] = True
data = readJSONheadedASCII(sorted(flist), convert=convdict)
omniout = SpaceData()
time_var = [var for var in ['DateTime', 'Time', 'Epoch', 'UTC'] if var in data]
if time_var:
use_t_var = time_var[0]
else:
#no obvious time variable in input files ... can't continue
raise ValueError('No clear time variable in file')
if kwargs['interp'] is True:
data['RDT'] = spt.Ticktock(data[use_t_var]).RDT
keylist = sorted(data.keys())
dum = keylist.pop(keylist.index(use_t_var))
for key in keylist:
try:
omniout[key] = dmarray(np.interp(ticks.RDT, data['RDT'], data[key], left=np.NaN, right=np.NaN))
omniout[key].attrs = dmcopy(data[key].attrs)
except:
try:
omniout[key] = dmfilled([len(ticks.RDT), data[key].shape[1]], fillval=np.NaN, attrs=dmcopy(data[key].attrs))
for col in range(data[key].shape[1]):
omniout[key][:,col] = np.interp(ticks.RDT, data['RDT'], data[key][:,col], left=np.NaN, right=np.NaN)
except ValueError:
print('Failed to interpolate {0} to new time base, skipping variable'.format(key))
except IndexError:
print('Variable {0} appears to be non-record varying, skipping interpolation'.format(key))
omniout[key] = data[key]
omniout['UTC'] = ticks.UTC
else:
#Trim to specified times
inds = tOverlapHalf([ticks[0].RDT, ticks[-1].RDT], spt.Ticktock(data['DateTime']).RDT)
for key in data:
if len(inds) == len(data[key]):
omniout[key] = data[key][inds]
else: #is ancillary data
omniout[key] = data[key]
#TODO: convert to same format as OMNI/QD read (or vice versa)
omniout['UTC'] = omniout[use_t_var]
return omniout
else:
raise IOError('Specified dbase ({0}) must be specified in spacepy.config'.format(dbase))
def getattrs(hf, key):
out = {}
if hasattr(hf[key],'attrs'):
for kk, value in hf[key].attrs.items():
try:
out[kk] = value
except:
pass
return out
def HrFromDT(indt):
hour = indt.hour
minute = indt.minute
second = indt.second
musecond = indt.microsecond
return hour+(minute/60.0)+(second/3600.0)+(musecond/3600.0e3)
import h5py as h5
fname, QDkeylist, O2keylist = '', [], []
omnivals = SpaceData()
dbase_select = dbase_options[dbase]
if dbase_select in [1, 3, -9]:
if dbase_select > 0:
ldb = 'QDhourly'
fln = omnifln
else:
ldb = 'Test'
fln = testfln
with h5.File(fln, 'r') as hfile:
QDkeylist = [kk for kk in hfile if kk not in ['Qbits', 'UTC']]
st, en = ticks[0].RDT, ticks[-1].RDT
##check that requested requested times are within range of data
enval, stval = omnirange(dbase=ldb)[1], omnirange(dbase=ldb)[0]
if (ticks.UTC[0]>enval) or (ticks[-1]<stval):
raise ValueError('Requested dates are outside data range')
if (ticks.UTC[-1]>enval) or (ticks[0]<stval):
print('Warning: Some requested dates are outside data range ({0})'.format(ldb))
inds = tOverlapHalf([st, en], hfile['RDT'], presort=True) #returns an xrange
inds = indsFromXrange(inds)
if inds[0] < 1: inds[0] = 1
sl_op = slice(inds[0]-1, inds[-1]+2)
fname = ','.join([fname,hfile.filename])
omnivals.attrs = getattrs(hfile, '/')
for key in QDkeylist:
omnivals[key] = dmarray(hfile[key][sl_op]) #TODO: add attrs from h5
omnivals[key].attrs = getattrs(hfile, key)
for key in hfile['Qbits']:
omnivals['Qbits<--{0}'.format(key)] = dmarray(hfile['/Qbits/{0}'.format(key)][sl_op])
omnivals['Qbits<--{0}'.format(key)].attrs = getattrs(hfile, '/Qbits/{0}'.format(key))
QDkeylist.append('Qbits<--{0}'.format(key))
if dbase_options[dbase] == 2 or dbase_options[dbase] == 3:
ldb = 'OMNI2hourly'
with h5.File(omni2fln) as hfile:
O2keylist = [kk for kk in hfile if kk not in ['Epoch','RDT']]
st, en = ticks[0].RDT, ticks[-1].RDT
##check that requested requested times are within range of data
enval, stval = omnirange(dbase=ldb)[1], omnirange(dbase=ldb)[0]
if (ticks[0].UTC>enval) or (ticks[-1]<stval):
raise ValueError('Requested dates are outside data range')
if (ticks[-1].UTC>enval) or (ticks[0]<stval):
print('Warning: Some requested dates are outside data range ({0})'.format(ldb))
inds = tOverlapHalf([st, en], hfile['RDT'], presort=True) #returns an xrange
inds = indsFromXrange(inds)
if inds[0] < 1: inds[0] = 1
sl_op = slice(inds[0]-1, inds[-1]+2)
fname = ','.join([fname,hfile.filename])
omnivals.attrs = getattrs(hfile, '/') #TODO: This overwrites the previous set on Merged load... Fix!
omnivals['RDT_OMNI'] = dmarray(hfile['RDT'][sl_op])
for key in O2keylist:
omnivals[key] = dmarray(hfile[key][sl_op]) #TODO: add attrs from h5
omnivals[key].attrs = getattrs(hfile, key)
if dbase_options[dbase] == 3:
#prune "merged" SpaceData
sigmas = [key for key in omnivals if 'sigma' in key]
for sk in sigmas: del omnivals[sk]
bees = [key for key in omnivals if re.search('B._', key)]
for bs in bees: del omnivals[bs]
aves = [key for key in omnivals if ('_ave' in key) or ('ave_' in key)]
for av in aves: del omnivals[av]
omniout = SpaceData(attrs=dmcopy(omnivals.attrs))
omniout.attrs['filename'] = fname[1:]
###print('QDkeys: {0}\n\nO2keys: {1}'.format(QDkeylist, O2keylist))
for key in sorted(omnivals.keys()):
if key in O2keylist:
omniout[key] = dmarray(np.interp(ticks.RDT, omnivals['RDT_OMNI'], omnivals[key], left=np.NaN, right=np.NaN))
#set metadata -- assume this has been set properly in d/l'd file to match ECT-SOC files
omniout[key].attrs = dmcopy(omnivals[key].attrs)
elif key in QDkeylist:
omniout[key] = dmarray(np.interp(ticks.RDT, omnivals['RDT'], omnivals[key], left=np.NaN, right=np.NaN))
omniout[key].attrs = dmcopy(omnivals[key].attrs)
if key == 'G3': #then we have all the Gs
omniout['G'] = dmarray(np.vstack([omniout['G1'], omniout['G2'], omniout['G3']]).T)
omniout['G'].attrs = dmcopy(omnivals['G1'].attrs)
for i in range(1,4): del omniout['G{0}'.format(i)]
if key == 'W6':
omniout['W'] = dmarray(np.vstack([omniout['W1'], omniout['W2'], omniout['W3'], omniout['W4'], omniout['W5'], omniout['W6']]).T)
omniout['W'].attrs = dmcopy(omnivals['W1'].attrs)
for i in range(1,7): del omniout['W{0}'.format(i)]
if 'Qbits' in key:
#Qbits are integer vals, higher is better, so floor to get best representation of interpolated val
omniout[key] = np.floor(omnivals[key])
omniout[key].attrs = dmcopy(omnivals[key].attrs)
if 'G3' in key: #then we have all the Gs
omniout['Qbits<--G'] = dmarray(np.vstack([omniout['Qbits<--G1'], omniout['Qbits<--G2'], omniout['Qbits<--G3']]).T)
for i in range(1,4): del omniout['Qbits<--G{0}'.format(i)]
if 'W6' in key:
omniout['Qbits<--W'] = dmarray(np.vstack([omniout['Qbits<--W1'], omniout['Qbits<--W2'], omniout['Qbits<--W3'], omniout['Qbits<--W4'], omniout['Qbits<--W5'], omniout['Qbits<--W6']]).T)
for i in range(1,7): del omniout['Qbits<--W{0}'.format(i)]
omniout['ticks'] = ticks
omniout['UTC'] = ticks.UTC
omniout['Hr'] = dmarray([HrFromDT(val) for val in omniout['UTC']])
omniout['Year'] = dmarray([val.year for val in omniout['UTC']])
omniout = unflatten(omniout)
return omniout
def test_tOverlapHalfSorted(self):
"""Get overlap of only one list, exploiting the sort"""
real_ans = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
ans = tb.tOverlapHalf(self.dt_a, self.dt_b, presort=True)
numpy.testing.assert_array_equal(real_ans, ans)
def makeSW_v2():
"""Construct initial estimate of hourly solar wind parameters"""
st = dt.datetime(1989, 3, 12)
en = dt.datetime(1989, 3, 15)
hourly = getKondrashovSW()
# Keep IMF By and n, set V to Boteler/Nagatsuma
t_ssc1 = dt.datetime(1989, 3, 13, 1, 27)
t_ssc2 = dt.datetime(1989, 3, 13, 7, 43)
t_cme = dt.datetime(1989, 3, 13, 16)
t_turn = dt.datetime(1989, 3, 14, 2)
# Set Bx positive, in accordance with ISEE-3 data, Vy/Vz->0
hourly['Bx'] = dm.dmfilled(hourly['By'].shape, fillval=3)
hourly['Vy'] = dm.dmfilled(hourly['By'].shape, fillval=0)
hourly['Vz'] = dm.dmfilled(hourly['By'].shape, fillval=0)
# Before first SSC
inds_before_1 = tb.tOverlapHalf([dt.datetime(1989, 3, 12), t_ssc1],
hourly['DateTime'])
hourly['V_sw'][inds_before_1] = 400
# Between first and ssecond SSC
inds_between_12 = tb.tOverlapHalf([t_ssc1, t_ssc2], hourly['DateTime'])
hourly['V_sw'][inds_between_12] = 550
# IMF turns north around 1989-03-14T02:00:00 according to inverse Burton and Kondrashov
inds_mainphase = tb.tOverlapHalf([t_ssc2, t_turn], hourly['DateTime'])
hourly['V_sw'][inds_mainphase] = 983
# Then have speed decay towards IMP-8 measurement which is ballpark 820 km/s
inds_rest = tb.tOverlapHalf([t_turn, hourly['DateTime'][-1]],
hourly['DateTime'])
hourly['V_sw'][inds_rest] = np.linspace(983, 820, len(inds_rest))
# Now we have speed, estimate temperature
hourly['Plasma_temp'] = emp.getExpectedSWTemp(hourly['V_sw'],
model='XB15',
units='K')
inds_cme = tb.tOverlapHalf([t_cme, en], hourly['DateTime'])
hourly['Plasma_temp'][
inds_cme] /= 3 # reduce by factor of 3 for CME-like temp
# Get "Kondrashov VBs" using V from Boteler/Nagatsuma
hourly['VBs_K'] = 1e-3 * hourly['V_sw'] * rectify(
-1 * hourly['Bz']) # mV/m
# Now get VBs from inverse Burton
ky_dat = kyo.fetch('dst', (st.year, st.month, st.day),
(en.year, en.month, en.day))
inds = tb.tOverlapHalf([st, en], ky_dat['time'])
# Substitute density curve from Sept. 2017 (double shock)
sep17 = pybats.ImfInput(
filename=
'/home/smorley/projects/github/advect1d/IMF_201709_advect_filt.dat',
load=True)
den_inds = tb.tOverlapHalf(
[t_ssc1 - dt.timedelta(hours=25), hourly['DateTime'][-1]],
hourly['DateTime'])
nhours = len(den_inds)
# Keep the opening 8 hours from Kondrashov (2017 event gets high)
hourly['Den_P'][den_inds[9:]] = 2 + (sep17['rho'][::60][9:nhours] * 2)
# After shocks, ensure number density doesn't drop below 10 (keeps M_A over 2)
after_ssc2 = hourly['DateTime'] > t_ssc2
under_lim = hourly['Den_P'] <= 10
limit_inds = np.logical_and(after_ssc2, under_lim)
hourly['Den_P'][limit_inds] = 10
# Pressure correct here using n and V
hourly = calc_P(hourly)
hourly['Dst'] = ky_dat['dst'][inds]
dst_star = emp.getDststar(hourly, model='OBrien')
hourly['VBs_OB'] = 1e-3 * inverseOBrienMcPherron(dst_star)
# Make new Bz from VBs_OB
hourly['Bz_OB'] = -1e3 * hourly['VBs_OB'] / hourly['V_sw'] # nT
return hourly
def test_tOverlapHalf(self):
"""Get overlap of only one list"""
real_ans = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
ans = tb.tOverlapHalf(self.dt_a, self.dt_b)
self.assertEqual(real_ans, ans)
def plotSummary(self,
timerange=None,
coord_sys=None,
fig_target=None,
spec=False,
orbit_params=(False, True),
**kwargs):
"""Generate summary plot of AE9/AP9/SPM data loaded
spec : if True, plot spectrogram instead of flux/fluence lineplot, requires 'ecol' keyword
"""
if timerange:
if isinstance(timerange, spt.Ticktock):
t1 = timerange.UTC[0]
t2 = timerange.UTC[-1]
elif isinstance(timerange[0], dt.datetime):
t1 = timerange[0]
t2 = timerange[-1]
else:
raise TypeError('Incorrect data type provided for timerange')
# now select subset
i_use = tb.tOverlapHalf([t1, t2], self['Epoch'])
t_use = self['Epoch'][i_use]
c_use = self['Coords'][i_use]
f_use = self[self.attrs['varname']][i_use, ...]
else:
t_use = self['Epoch']
c_use = self['Coords']
f_use = self[self.attrs['varname']]
if coord_sys and (coord_sys.upper() != c_use.attrs['COORD_SYS']):
# TODO: We assume cartesian, make flexible so can take spherical
cx = spc.Coords(c_use, c_use.attrs['COORD_SYS'], 'car')
cx.ticks = spt.Ticktock(t_use)
cx = cx.convert(coord_sys.upper(), 'car').data
else:
coord_sys = c_use.attrs['COORD_SYS']
cx = c_use
sys_subs = r'$_{' + coord_sys + r'}$'
splot.style('spacepy')
if orbit_params[0]:
landscape = orbit_params[1]
locs = [121, 122] if landscape else [211, 212]
else:
locs = [223, 224]
landscape = True
if fig_target:
fig, ax1 = splot.set_target(fig_target, loc=locs[0])
else:
fig, ax1 = splot.set_target(fig_target,
figsize=(8, 8),
loc=locs[0])
fig, ax2 = splot.set_target(fig, loc=locs[1])
ax1 = self._makeOrbitAxis(cx[:, 0], cx[:, 1], ax1)
ax2 = self._makeOrbitAxis(cx[:, 0], cx[:, 2], ax2)
if landscape:
ax1.set_xlabel('X{0} [{1}]'.format(sys_subs,
self['Coords'].attrs['UNITS']))
ax2.set_xlabel('X{0} [{1}]'.format(sys_subs,
self['Coords'].attrs['UNITS']))
ax1.set_ylabel('Y{0} [{1}]'.format(sys_subs,
self['Coords'].attrs['UNITS']))
ax2.set_ylabel('Z{0} [{1}]'.format(sys_subs,
self['Coords'].attrs['UNITS']))
ax1xl, ax1yl, ax2xl, ax2yl = ax1.get_xlim(), ax1.get_ylim(
), ax2.get_xlim(), ax2.get_ylim()
maxabslim = np.max(np.abs([ax1xl, ax1yl, ax2xl, ax2yl]))
if np.abs((ax1.get_xlim()[1] - ax1.get_xlim()[0])) < 1:
refpt = maxabslim
ax1.set_xlim([-1.25 * refpt, 1.25 * refpt])
ax1.set_ylim(ax1.get_xlim())
refpt = ax2.get_xlim()[0]
ax2.set_xlim([-1.25 * refpt, 1.25 * refpt])
ax2.set_ylim(ax1.get_xlim())
else:
ax1.set_xlim([-maxabslim, maxabslim])
ax1.set_ylim([-maxabslim, maxabslim])
ax2.set_xlim([-maxabslim, maxabslim])
ax2.set_ylim(ax2.get_xlim())
ax1.invert_yaxis()
ax1.invert_xaxis()
ax2.invert_xaxis()
ax1.set_aspect('equal')
ax2.set_aspect('equal')
if not orbit_params[0]:
ax3 = splot.plt.subplot2grid((2, 2), (0, 0), colspan=2)
if not spec:
l3 = ax3.semilogy(t_use, f_use)
ylab = '{0} ['.format(self.attrs['varname']) + re.sub(
'(\^[\d|-]*)+', _grp2mathmode,
self[self.attrs['varname']].attrs['UNITS']) + ']'
ax3.set_ylabel(ylab)
for ll, nn in zip(l3, self['Energy']):
ll.set_label('{0} {1}'.format(
nn, self['Energy'].attrs['UNITS']))
ncol = len(self['Energy']) // 2 if len(
self['Energy']) <= 6 else 3
leg = ax3.legend(loc='center',
bbox_to_anchor=(0.5, 1),
ncol=ncol,
frameon=True,
framealpha=0.5)
lims3 = ax3.get_ylim()
newupper = 10**(np.log10(lims3[0]) +
(np.log10(lims3[1] / lims3[0]) * 1.125))
ax3.set_ylim([lims3[0], newupper])
splot.applySmartTimeTicks(ax3, t_use)
fig.tight_layout()
pos3 = ax3.get_position()
ax3.set_position(
[pos3.x0, pos3.y0, pos3.width, pos3.height * 0.8])
# fig.suptitle('{model_type}\n'.format(**self.attrs) +
# '{0} - {1}'.format(t_use[0].isoformat()[:19], t_use[-1].isoformat()[:19]))
else:
if timerange:
raise NotImplementedError(
'Time range selection not yet implemented for spectrograms'
)
pos3 = ax3.get_position()
ax3.set_position(
[pos3.x0, pos3.y0, pos3.width, pos3.height * 0.9])
ecol = kwargs['ecol'] if 'ecol' in kwargs else 0
ax3 = self.plotSpectrogram(target=ax3, ecol=ecol)
splot.plt.subplots_adjust(wspace=0.3)
pos1 = ax1.get_position()
ax1.set_position([pos3.x0, pos1.y0, pos1.width, pos1.height])
splot.revert_style()
return fig
import datetime
import numpy as np
import spacepy.toolbox as tb
import spacepy.pycdf as pycdf
from spacepy.datamodel import SpaceData, dmarray
cdf = pycdf.CDF('poes_n15_short.cdf')
plotTime = np.asarray(
[datetime.datetime(2000, 11, 1),
datetime.datetime(2000, 12, 1)])
rng = tb.tOverlapHalf(plotTime, cdf['EPOCH'], presort=True)
ind1 = rng[0]
ind2 = rng[-1]
data = SpaceData()
data['Epoch'] = dmarray(cdf['EPOCH'][ind1:ind2])
data['lValue'] = dmarray(cdf['lValue'][ind1:ind2])
data['mepOmni'] = dmarray(cdf['mepOmni'][ind1:ind2])[:, 0]
#data['Epoch'] = cdf['EPOCH'][ind1:ind2]
#data['lValue'] = cdf['lValue'][ind1:ind2]
cdf.close()
avgt = datetime.timedelta(minutes=300) #time scale to average over in minutes
Tbins = np.asarray([
plotTime[0] + stp * avgt for stp in xrange(
np.long(np.ceil(plotTime.ptp().total_seconds() /
def get_omni(ticks, dbase='QDhourly', **kwargs):
'''
Returns Qin-Denton OMNI values, interpolated to any time-base from a default hourly resolution
The update function in toolbox retrieves all available hourly Qin-Denton data,
and this function accesses that and interpolates to the given times,
returning the OMNI values as a SpaceData (dict-like) with
Kp, Dst, dens, velo, Pdyn, ByIMF, BzIMF, G1, G2, G3, etc.
(see also http://www.dartmouth.edu/~rdenton/magpar/index.html and
http://www.agu.org/pubs/crossref/2007/2006SW000296.shtml )
Parameters
==========
ticks : Ticktock class or array-like of datetimes
time values for desired output
dbase : str (optional)
Select data source, options are 'QDhourly', 'OMNI2hourly', 'Mergedhourly'
Note - Custom data sources can be specified in the spacepy config file
as described in the module documentation.
Returns
=======
out : spacepy.datamodel.SpaceData
containing all Qin-Denton values at times given by ticks
Examples
========
>>> import spacepy.time as spt
>>> import spacepy.omni as om
>>> ticks = spt.Ticktock(['2002-02-02T12:00:00', '2002-02-02T12:10:00'], 'ISO')
>>> d = om.get_omni(ticks)
>>> d.tree(levels=1)
+
|____ByIMF
|____Bz1
|____Bz2
|____Bz3
|____Bz4
|____Bz5
|____Bz6
|____BzIMF
|____DOY
|____Dst
|____G1
|____G2
|____G3
|____Hr
|____Kp
|____Pdyn
|____Qbits
|____RDT
|____UTC
|____W1
|____W2
|____W3
|____W4
|____W5
|____W6
|____Year
|____akp3
|____dens
|____ticks
|____velo
Notes
=====
Note about Qbits: If the status variable is 2, the quantity you are using is fairly well
determined. If it is 1, the value has some connection to measured values, but is not directly
measured. These values are still better than just using an average value, but not as good
as those with the status variable equal to 2. If the status variable is 0, the quantity is
based on average quantities, and the values listed are no better than an average value. The
lower the status variable, the less confident you should be in the value.
'''
dbase_options = {
'QDhourly': 1,
'OMNI2hourly': 2,
'Mergedhourly': 3,
'Test': -9,
}
if not isinstance(ticks, spt.Ticktock):
try:
ticks = spt.Ticktock(ticks, 'UTC')
except:
raise TypeError(
'get_omni: Input times must be a Ticktock object or a list of datetime objects'
)
if not dbase in dbase_options:
from spacepy import config
if dbase in config:
#If a dbase is specified that isn't a default, then it MUST be in the spacepy config
qdpath = os.path.split(os.path.split(config[dbase])[0])[0]
if not os.path.isdir(qdpath):
raise IOError(
'Specified dbase ({0}) does not have a valid location ({1})'
.format(dbase, config[dbase]))
days = list(set([tt.date() for tt in ticks.UTC]))
flist = [''] * len(days)
fnpath, fnformat = os.path.split(config[dbase])
for idx, day in enumerate(days):
dp = fnpath.replace('YYYY', '{0}'.format(day.year))
df = fnformat.replace('YYYY', '{0}'.format(day.year))
df = df.replace('MM', '{0:02d}'.format(day.month))
df = df.replace('DD', '{0:02d}'.format(day.day))
flist[idx] = os.path.join(dp, df)
if 'convert' in kwargs:
convdict = kwargs['convert']
else:
convdict = True #set to True as default?
if 'interp' not in kwargs:
kwargs['interp'] = True
data = readJSONheadedASCII(sorted(flist), convert=convdict)
omniout = SpaceData()
time_var = [
var for var in ['DateTime', 'Time', 'Epoch', 'UTC']
if var in data
]
if time_var:
use_t_var = time_var[0]
else:
#no obvious time variable in input files ... can't continue
raise ValueError('No clear time variable in file')
if kwargs['interp'] is True:
data['RDT'] = spt.Ticktock(data[use_t_var]).RDT
keylist = sorted(data.keys())
dum = keylist.pop(keylist.index(use_t_var))
for key in keylist:
try:
omniout[key] = dmarray(
np.interp(ticks.RDT,
data['RDT'],
data[key],
left=np.NaN,
right=np.NaN))
omniout[key].attrs = dmcopy(data[key].attrs)
except:
try:
omniout[key] = dmfilled(
[len(ticks.RDT), data[key].shape[1]],
fillval=np.NaN,
attrs=dmcopy(data[key].attrs))
for col in range(data[key].shape[1]):
omniout[key][:,
col] = np.interp(ticks.RDT,
data['RDT'],
data[key][:,
col],
left=np.NaN,
right=np.NaN)
except ValueError:
print(
'Failed to interpolate {0} to new time base, skipping variable'
.format(key))
except IndexError:
print(
'Variable {0} appears to be non-record varying, skipping interpolation'
.format(key))
omniout[key] = data[key]
omniout['UTC'] = ticks.UTC
else:
#Trim to specified times
inds = tOverlapHalf([ticks[0].RDT, ticks[-1].RDT],
spt.Ticktock(data['DateTime']).RDT)
for key in data:
if len(inds) == len(data[key]):
omniout[key] = data[key][inds]
else: #is ancillary data
omniout[key] = data[key]
#TODO: convert to same format as OMNI/QD read (or vice versa)
omniout['UTC'] = omniout[use_t_var]
return omniout
else:
raise IOError(
'Specified dbase ({0}) must be specified in spacepy.config'.
format(dbase))
def getattrs(hf, key):
out = {}
if hasattr(hf[key], 'attrs'):
for kk, value in hf[key].attrs.items():
try:
out[kk] = value
except:
pass
return out
def HrFromDT(indt):
hour = indt.hour
minute = indt.minute
second = indt.second
musecond = indt.microsecond
return hour + (minute / 60.0) + (second / 3600.0) + (musecond /
3600.0e3)
import h5py as h5
fname, QDkeylist, O2keylist = '', [], []
omnivals = SpaceData()
dbase_select = dbase_options[dbase]
if dbase_select in [1, 3, -9]:
if dbase_select > 0:
ldb = 'QDhourly'
fln = omnifln
else:
ldb = 'Test'
fln = testfln
with h5.File(fln, 'r') as hfile:
QDkeylist = [kk for kk in hfile if kk not in ['Qbits', 'UTC']]
st, en = ticks[0].RDT, ticks[-1].RDT
##check that requested requested times are within range of data
enval, stval = omnirange(dbase=ldb)[1], omnirange(dbase=ldb)[0]
if (ticks.UTC[0] > enval) or (ticks[-1] < stval):
raise ValueError('Requested dates are outside data range')
if (ticks.UTC[-1] > enval) or (ticks[0] < stval):
print(
'Warning: Some requested dates are outside data range ({0})'
.format(ldb))
inds = tOverlapHalf([st, en], hfile['RDT'],
presort=True) #returns an xrange
inds = indsFromXrange(inds)
if inds[0] < 1: inds[0] = 1
sl_op = slice(inds[0] - 1, inds[-1] + 2)
fname = ','.join([fname, hfile.filename])
omnivals.attrs = getattrs(hfile, '/')
for key in QDkeylist:
omnivals[key] = dmarray(
hfile[key][sl_op]) #TODO: add attrs from h5
omnivals[key].attrs = getattrs(hfile, key)
for key in hfile['Qbits']:
omnivals['Qbits<--{0}'.format(key)] = dmarray(
hfile['/Qbits/{0}'.format(key)][sl_op])
omnivals['Qbits<--{0}'.format(key)].attrs = getattrs(
hfile, '/Qbits/{0}'.format(key))
QDkeylist.append('Qbits<--{0}'.format(key))
if dbase_options[dbase] == 2 or dbase_options[dbase] == 3:
ldb = 'OMNI2hourly'
with h5.File(omni2fln) as hfile:
O2keylist = [kk for kk in hfile if kk not in ['Epoch', 'RDT']]
st, en = ticks[0].RDT, ticks[-1].RDT
##check that requested requested times are within range of data
enval, stval = omnirange(dbase=ldb)[1], omnirange(dbase=ldb)[0]
if (ticks[0].UTC > enval) or (ticks[-1] < stval):
raise ValueError('Requested dates are outside data range')
if (ticks[-1].UTC > enval) or (ticks[0] < stval):
print(
'Warning: Some requested dates are outside data range ({0})'
.format(ldb))
inds = tOverlapHalf([st, en], hfile['RDT'],
presort=True) #returns an xrange
inds = indsFromXrange(inds)
if inds[0] < 1: inds[0] = 1
sl_op = slice(inds[0] - 1, inds[-1] + 2)
fname = ','.join([fname, hfile.filename])
omnivals.attrs = getattrs(
hfile, '/'
) #TODO: This overwrites the previous set on Merged load... Fix!
omnivals['RDT_OMNI'] = dmarray(hfile['RDT'][sl_op])
for key in O2keylist:
omnivals[key] = dmarray(
hfile[key][sl_op]) #TODO: add attrs from h5
omnivals[key].attrs = getattrs(hfile, key)
if dbase_options[dbase] == 3:
#prune "merged" SpaceData
sigmas = [key for key in omnivals if 'sigma' in key]
for sk in sigmas:
del omnivals[sk]
bees = [key for key in omnivals if re.search('B._', key)]
for bs in bees:
del omnivals[bs]
aves = [key for key in omnivals if ('_ave' in key) or ('ave_' in key)]
for av in aves:
del omnivals[av]
omniout = SpaceData(attrs=dmcopy(omnivals.attrs))
omniout.attrs['filename'] = fname[1:]
###print('QDkeys: {0}\n\nO2keys: {1}'.format(QDkeylist, O2keylist))
for key in sorted(omnivals.keys()):
if key in O2keylist:
omniout[key] = dmarray(
np.interp(ticks.RDT,
omnivals['RDT_OMNI'],
omnivals[key],
left=np.NaN,
right=np.NaN))
#set metadata -- assume this has been set properly in d/l'd file to match ECT-SOC files
omniout[key].attrs = dmcopy(omnivals[key].attrs)
elif key in QDkeylist:
omniout[key] = dmarray(
np.interp(ticks.RDT,
omnivals['RDT'],
omnivals[key],
left=np.NaN,
right=np.NaN))
omniout[key].attrs = dmcopy(omnivals[key].attrs)
if key == 'G3': #then we have all the Gs
omniout['G'] = dmarray(
np.vstack([omniout['G1'], omniout['G2'], omniout['G3']]).T)
omniout['G'].attrs = dmcopy(omnivals['G1'].attrs)
for i in range(1, 4):
del omniout['G{0}'.format(i)]
if key == 'W6':
omniout['W'] = dmarray(
np.vstack([
omniout['W1'], omniout['W2'], omniout['W3'], omniout['W4'],
omniout['W5'], omniout['W6']
]).T)
omniout['W'].attrs = dmcopy(omnivals['W1'].attrs)
for i in range(1, 7):
del omniout['W{0}'.format(i)]
if 'Qbits' in key:
#Qbits are integer vals, higher is better, so floor to get best representation of interpolated val
omniout[key] = np.floor(omnivals[key])
omniout[key].attrs = dmcopy(omnivals[key].attrs)
if 'G3' in key: #then we have all the Gs
omniout['Qbits<--G'] = dmarray(
np.vstack([
omniout['Qbits<--G1'], omniout['Qbits<--G2'],
omniout['Qbits<--G3']
]).T)
for i in range(1, 4):
del omniout['Qbits<--G{0}'.format(i)]
if 'W6' in key:
omniout['Qbits<--W'] = dmarray(
np.vstack([
omniout['Qbits<--W1'], omniout['Qbits<--W2'],
omniout['Qbits<--W3'], omniout['Qbits<--W4'],
omniout['Qbits<--W5'], omniout['Qbits<--W6']
]).T)
for i in range(1, 7):
del omniout['Qbits<--W{0}'.format(i)]
omniout['ticks'] = ticks
omniout['UTC'] = ticks.UTC
omniout['Hr'] = dmarray([HrFromDT(val) for val in omniout['UTC']])
omniout['Year'] = dmarray([val.year for val in omniout['UTC']])
omniout = unflatten(omniout)
return omniout
def plotSummary(self, timerange=None, coord_sys=None, fig_target=None, spec=False, orbit_params=(False, True),
**kwargs):
"""Generate summary plot of AE9/AP9/SPM data loaded
spec : if True, plot spectrogram instead of flux/fluence lineplot, requires 'ecol' keyword
"""
if timerange:
if isinstance(timerange, spt.Ticktock):
t1 = timerange.UTC[0]
t2 = timerange.UTC[-1]
elif isinstance(timerange[0], dt.datetime):
t1 = timerange[0]
t2 = timerange[-1]
else:
raise TypeError('Incorrect data type provided for timerange')
# now select subset
i_use = tb.tOverlapHalf([t1, t2], self['Epoch'])
t_use = self['Epoch'][i_use]
c_use = self['Coords'][i_use]
f_use = self[self.attrs['varname']][i_use, ...]
else:
t_use = self['Epoch']
c_use = self['Coords']
f_use = self[self.attrs['varname']]
if coord_sys and (coord_sys.upper() != c_use.attrs['COORD_SYS']):
# TODO: We assume cartesian, make flexible so can take spherical
cx = spc.Coords(c_use, c_use.attrs['COORD_SYS'], 'car')
cx.ticks = spt.Ticktock(t_use)
cx = cx.convert(coord_sys.upper(), 'car').data
else:
coord_sys = c_use.attrs['COORD_SYS']
cx = c_use
sys_subs = r'$_{' + coord_sys + r'}$'
splot.style('spacepy')
if orbit_params[0]:
landscape = orbit_params[1]
locs = [121, 122] if landscape else [211, 212]
else:
locs = [223, 224]
landscape = True
if fig_target:
fig, ax1 = splot.set_target(fig_target, loc=locs[0])
else:
fig, ax1 = splot.set_target(fig_target, figsize=(8, 8), loc=locs[0])
fig, ax2 = splot.set_target(fig, loc=locs[1])
ax1 = self._makeOrbitAxis(cx[:, 0], cx[:, 1], ax1)
ax2 = self._makeOrbitAxis(cx[:, 0], cx[:, 2], ax2)
if landscape: ax1.set_xlabel('X{0} [{1}]'.format(sys_subs, self['Coords'].attrs['UNITS']))
ax2.set_xlabel('X{0} [{1}]'.format(sys_subs, self['Coords'].attrs['UNITS']))
ax1.set_ylabel('Y{0} [{1}]'.format(sys_subs, self['Coords'].attrs['UNITS']))
ax2.set_ylabel('Z{0} [{1}]'.format(sys_subs, self['Coords'].attrs['UNITS']))
ax1xl, ax1yl, ax2xl, ax2yl = ax1.get_xlim(), ax1.get_ylim(), ax2.get_xlim(), ax2.get_ylim()
maxabslim = np.max(np.abs([ax1xl, ax1yl, ax2xl, ax2yl]))
if np.abs((ax1.get_xlim()[1] - ax1.get_xlim()[0])) < 1:
refpt = maxabslim
ax1.set_xlim([-1.25 * refpt, 1.25 * refpt])
ax1.set_ylim(ax1.get_xlim())
refpt = ax2.get_xlim()[0]
ax2.set_xlim([-1.25 * refpt, 1.25 * refpt])
ax2.set_ylim(ax1.get_xlim())
else:
ax1.set_xlim([-maxabslim, maxabslim])
ax1.set_ylim([-maxabslim, maxabslim])
ax2.set_xlim([-maxabslim, maxabslim])
ax2.set_ylim(ax2.get_xlim())
ax1.invert_yaxis()
ax1.invert_xaxis()
ax2.invert_xaxis()
ax1.set_aspect('equal')
ax2.set_aspect('equal')
if not orbit_params[0]:
ax3 = splot.plt.subplot2grid((2, 2), (0, 0), colspan=2)
if not spec:
l3 = ax3.semilogy(t_use, f_use)
ylab = '{0} ['.format(self.attrs['varname']) + re.sub('(\^[\d|-]*)+', _grp2mathmode,
self[self.attrs['varname']].attrs['UNITS']) + ']'
ax3.set_ylabel(ylab)
for ll, nn in zip(l3, self['Energy']):
ll.set_label('{0} {1}'.format(nn, self['Energy'].attrs['UNITS']))
ncol = len(self['Energy']) // 2 if len(self['Energy']) <= 6 else 3
leg = ax3.legend(loc='center', bbox_to_anchor=(0.5, 1), ncol=ncol,
frameon=True, framealpha=0.5)
lims3 = ax3.get_ylim()
newupper = 10 ** (np.log10(lims3[0]) + (np.log10(lims3[1] / lims3[0]) * 1.125))
ax3.set_ylim([lims3[0], newupper])
splot.applySmartTimeTicks(ax3, t_use)
fig.tight_layout()
pos3 = ax3.get_position()
ax3.set_position([pos3.x0, pos3.y0, pos3.width, pos3.height * 0.8])
# fig.suptitle('{model_type}\n'.format(**self.attrs) +
# '{0} - {1}'.format(t_use[0].isoformat()[:19], t_use[-1].isoformat()[:19]))
else:
if timerange: raise NotImplementedError('Time range selection not yet implemented for spectrograms')
pos3 = ax3.get_position()
ax3.set_position([pos3.x0, pos3.y0, pos3.width, pos3.height * 0.9])
ecol = kwargs['ecol'] if 'ecol' in kwargs else 0
ax3 = self.plotSpectrogram(target=ax3, ecol=ecol)
splot.plt.subplots_adjust(wspace=0.3)
pos1 = ax1.get_position()
ax1.set_position([pos3.x0, pos1.y0, pos1.width, pos1.height])
splot.revert_style()
return fig
fig, ax = plotMSBS(data=swdata89, angles=(-145, 145), mp=False, bs=True)
# add IMP8 traj
import os
import datetime as dt
import swtools
import utils
import spacepy.datamanager as dman
import spacepy.toolbox as tb
datapath = os.path.abspath(os.path.join('..', 'ref_data', '1989'))
# When plotting, use spacepy.datamanager to insert fill between contiguous regions
mit_pl = swtools.readIMP8plasmafile(os.path.join(datapath, 'imp8.data.1989.060.090'))
# Now insert fill at gaps for plotting positions
mit_t, mit_x = dman.insert_fill(np.asarray(mit_pl['time']), np.asarray(mit_pl['pos_gse'][:,0]))
mit_t, mit_y = dman.insert_fill(np.asarray(mit_pl['time']), np.asarray(mit_pl['pos_gse'][:,1]))
mit_t, mit_z = dman.insert_fill(np.asarray(mit_pl['time']), np.asarray(mit_pl['pos_gse'][:,2]))
inds13 = tb.tOverlapHalf([dt.datetime(1989,3,13), dt.datetime(1989,3,14)], mit_t)
inds14 = tb.tOverlapHalf([dt.datetime(1989,3,14), dt.datetime(1989,3,15)], mit_t)
inds1314 = tb.tOverlapHalf([dt.datetime(1989,3,13), dt.datetime(1989,3,15)], mit_t)
# Get position in aberrated GSE to compare to bow shock model
pos_agse1314 = np.zeros((len(mit_x),3))
r_sat_agse = np.zeros_like(mit_x)
r_bs_agse = np.zeros_like(mit_x)
xhat = np.array([1, 0, 0])
for idx in inds1314:
pos_gse1314 = np.array([mit_x[idx], mit_y[idx], mit_z[idx]])
pos_agse1314[idx] = agse_convert(pos_gse1314, swdata89)
r_sat_agse[idx] = np.linalg.norm(pos_agse1314[idx])
sattheta = utils.angle_between_vectors(xhat, pos_agse1314[idx])
r_bs_agse[idx] = bowshock(swdata89, sattheta)
# And now plot the orbit
ax.plot(mit_x[inds13], mit_y[inds13], label='IMP8 3/13')
import datetime
import numpy as np
import spacepy.toolbox as tb
import spacepy.pycdf as pycdf
from spacepy.datamodel import SpaceData, dmarray
cdf = pycdf.CDF('poes_n15_short.cdf')
plotTime = np.asarray([datetime.datetime(2000, 11, 1), datetime.datetime(2000, 12, 1)])
rng = tb.tOverlapHalf(plotTime, cdf['EPOCH'], presort=True)
ind1 = rng[0]
ind2 = rng[-1]
data = SpaceData()
data['Epoch'] = dmarray(cdf['EPOCH'][ind1:ind2])
data['lValue'] = dmarray(cdf['lValue'][ind1:ind2])
data['mepOmni'] = dmarray(cdf['mepOmni'][ind1:ind2])[:,0]
#data['Epoch'] = cdf['EPOCH'][ind1:ind2]
#data['lValue'] = cdf['lValue'][ind1:ind2]
cdf.close()
avgt = datetime.timedelta(minutes=300) #time scale to average over in minutes
Tbins = np.asarray([plotTime[0] + stp * avgt for
stp in xrange(np.long(np.ceil(plotTime.ptp().total_seconds()/avgt.total_seconds())))])
