def setUp(self):
# Files to open:
self.file_single = os.path.join(spacepy_testing.datadir, 'pybats_test',
'imf_single.dat')
self.file_multi = os.path.join(spacepy_testing.datadir, 'pybats_test',
'imf_multi.dat')
self.sing = pb.ImfInput(self.file_single)
self.mult = pb.ImfInput(self.file_multi)
def setUp(self):
self.pth = os.path.dirname(os.path.abspath(__file__))
# Files to open:
self.file_single = os.path.join(self.pth, 'data', 'pybats_test',
'imf_single.dat')
self.file_multi = os.path.join(self.pth, 'data', 'pybats_test',
'imf_multi.dat')
self.sing = pb.ImfInput(self.file_single)
self.mult = pb.ImfInput(self.file_multi)
def main(infiles=None):
# read SWMF ImfInput file
# Originally written to examine data from simulations
# by Morley, Welling and Woodroffe (2018). See data at
# Zenodo (https://doi.org/10.5281/zenodo.1324562)
infilename = 'Event5Ensembles/run_orig/IMF.dat'
eventIMF = bats.ImfInput(filename=infilename)
data1 = bats.LogFile('Event5Ensembles/run_orig/GM/IO2/log_e20100404-190000.log')
# #read IMF for 10 events...
# infiles = ['Event5Ensembles/run_{:03d}/IMF.dat'.format(n)
# for n in [32,4,36,10,13,17,18,20,24,29]]
# read IMF files for all ensemble members
if infiles is None:
infiles = glob.glob('Event5Ensembles/run_???/IMF.dat')
subsetlabel = False
else:
# got list of run directories from Dst/Kp plotter
subsetlabel = True
infiles = [os.path.join(d, 'IMF.dat') for d in infiles]
nsubset = len(infiles)
eventlist = [bats.ImfInput(filename=inf) for inf in infiles]
tstart = eventIMF['time'][0]
tstop = eventIMF['time'][-1]
sym = kyo.KyotoSym(lines=bats.kyoto.symfetch(tstart, tstop))
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
for ev in eventlist:
gco = '{}'.format(np.random.randint(5, 50)/100.0)
pred01B = Dst_Burton(sym['sym-h'][0], ev['ux'], ev['bz'], dt=1./60)
pred01O = Dst_OBrien(sym['sym-h'][0], ev['ux'], ev['bz'], dt=1./60)
ax1.plot(ev['time'], pred01B, c=gco, alpha=0.5)
ax2.plot(ev['time'], pred01O, c=gco, alpha=0.5)
# ax1.plot(sym['time'], sym['sym-h'], lw=1.5, c='crimson', label='Sym-H')
evtime = spt.Ticktock(eventIMF['time']).RDT
datime = spt.Ticktock(data1['time']).RDT
simDst = tb.interpol(evtime, datime, data1['dst'])
# ax1.plot(eventIMF['time'], simDst+11-(7.26*eventIMF['pram']), lw=1.5, c='seagreen', label='Sym-H (Press.Corr.)')
# ax2.plot(sym['time'], sym['sym-h'], lw=1.5, c='crimson')
# ax2.plot(eventIMF['time'], simDst+11-(7.26*eventIMF['pram']), lw=1.5, c='seagreen', label='Sym-H (Press.Corr.)')
ax1.plot(data1['time'], data1['dst'], linewidth=1.5, color='crimson', alpha=0.65, label='SWMF')
ax2.plot(data1['time'], data1['dst'], linewidth=1.5, color='crimson', alpha=0.65)
ax1.legend()
splot.applySmartTimeTicks(ax1, [tstart, tstop])
splot.applySmartTimeTicks(ax2, [tstart, tstop], dolabel=True)
ax1.set_ylabel('Sym-H [nT]')
ax2.set_ylabel('Sym-H [nT]')
ax1.text(0.05, 0.05, "Burton et al.", transform=ax1.transAxes)
ax2.text(0.05, 0.05, "O'Brien et al.", transform=ax2.transAxes)
def makeImfInput(indata, fname=None, keymap=None):
"""Make an SWMF IMF input file from an input SpaceData"""
if keymap is None:
keymap = {
'DateTime': 'time',
'Bx': 'bx',
'By': 'by',
'Bz_OB': 'bz',
'V_sw': 'ux',
'Vy': 'uy',
'Vz': 'uz',
'Den_P': 'rho',
'Plasma_temp': 'temp',
}
numpts = indata['DateTime'].shape[0]
swmfdata = pybats.ImfInput(filename=False, load=False, npoints=numpts)
for key_o, newkey in keymap.items():
if newkey == 'ux':
swmfdata[newkey] = -1 * np.abs(dm.dmcopy(indata[key_o]))
else:
swmfdata[newkey] = dm.dmcopy(indata[key_o])
swmfdata.attrs['coor'] = 'GSM'
if fname is not None:
swmfdata.write(fname)
return swmfdata
def testSubMillisec(self):
'''
Test case where sub-millisecond time values can create problems on
read/write.
'''
# Create an IMF object from scratch, fill with zeros.
imf = pb.ImfInput()
for key in imf:
imf[key] = [0]
# Add a sub-millisecond time:
imf['time'] = [dt.datetime(2017, 9, 6, 16, 42, 36, 999600)]
# Write and test for non-failure on re-read:
imf.write('testme.tmp')
imf2 = pb.ImfInput('testme.tmp')
# Test for floor of sub-millisecond times:
self.assertEqual(self.knownSubMilli, imf2['time'][0])
def testWrite(self):
# Test that files are correctly written to file.
from numpy.testing import assert_array_equal as assert_array
# Save original file names:
old_file_1 = self.sing.attrs['file']
old_file_2 = self.mult.attrs['file']
# Rename files, write:
self.sing.attrs['file'] = './imf_sing.tmp'
self.mult.attrs['file'] = './imf_mult.tmp'
self.sing.write()
self.mult.write()
# Reopen files:
sing = pb.ImfInput('./imf_sing.tmp')
mult = pb.ImfInput('./imf_mult.tmp')
# Ensure files were written correctly:
for v in sing:
assert_array(self.sing[v], sing[v])
for v in mult:
assert_array(self.mult[v], mult[v])
def setUp(self):
self.sing = pb.ImfInput(self.file_single)
self.mult = pb.ImfInput(self.file_multi)
def process_imf(fpath, rate): orig_imf = pybats.ImfInput(fpath) interped_imf = interp_imf(orig_imf) proc_imf = imf_ds(interped_imf, rate) return proc_imf
plt.tight_layout()
adjust_plots(a5, '$|V_{X}|$ ($km/s$)', Zero=False, xlab=True)
return fig
def process_imf(fpath, rate):
orig_imf = pybats.ImfInput(fpath)
interped_imf = interp_imf(orig_imf)
proc_imf = imf_ds(interped_imf, rate)
return proc_imf
if __name__ == "__main__":
rate = int(sys.argv[1])
fpath = sys.argv[2]
imf = pybats.ImfInput(fpath)
new_imf = pybats.ImfInput(fpath)
interped_imf = interp_imf(new_imf)
new_imf = imf_ds(interped_imf, rate)
five_imf = pybats.ImfInput('/Users/sgraf/Desktop/HydroQuebecRemix/swmf_input/Event20110805/IMF_5min.dat')
thirty_imf = pybats.ImfInput('/Users/sgraf/Desktop/HydroQuebecRemix/swmf_input/Event20110805/IMF_30min.dat')
hour_imf = pybats.ImfInput('/Users/sgraf/Desktop/HydroQuebecRemix/swmf_input/Event20110805/IMF_hourly.dat')
interped_imf = pybats.ImfInput('/Users/sgraf/Desktop/HydroQuebecRemix/swmf_input/Event20110805/IMF_interpolated.dat')
fig = plot_for_dan([interped_imf,five_imf,thirty_imf,hour_imf])
plt.show()
imf.quicklook().savefig('original_imf.png')
# No imf file given? Let's try to find one.
if not imffile:
if glob('imf*.dat'):
imffile = glob('imf*.dat')[0]
elif glob('IMF*.dat'):
imffile = glob('IMF*.dat')[0]
elif glob('../imf*.dat'):
imffile = glob('../imf*.dat')[0]
elif glob('../IMF*.dat'):
imffile = glob('../IMF*.dat')[0]
else:
raise (ValueError("Could not find IMF file. Use -i flag."))
print('\tIMF File: using {}'.format(imffile))
# Obtain imf and log files. Skip steady-state log files.
imf = pb.ImfInput(imffile)
for logname in glob(prefix + 'log_*.log'):
log = pbs.BatsLog(logname, starttime=start)
if log['runtime'][-1] != log['runtime'][0]: break
# Did we find a log file?
if 'log' not in locals():
raise (ValueError("Did not find MHD Logfile (required)."))
print('\tLog File: using {}'.format(logname))
# Get observed DST. Raise exception if we cannot.
if args.obsdst:
if not log.fetch_obs_dst():
raise ValueError('Failed to obtain observed Dst. Is KyotoWDC down?')
imf.calc_pram()
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
dt = iterate(state, t, outdata, l1data_tnum, output_x=output_x)
t += dt
i += 1
# Convert timesteps to datetimes
outdata['time'] = [
starttime + timedelta(seconds=n) for n in outdata['time']
]
# Set up pram and temp keys
outdata['pram_1'] = np.multiply(outdata['ux'], outdata['ux'])
outdata['pram_2'] = np.multiply(outdata['pram_1'], outdata['rho'])
outdata['pram'] = 1.67621e-6 * outdata['pram_2']
outdata['temp'] = outdata['T']
# Set up dictionary
imf = pybats.ImfInput(load=False)
for key in imf.keys():
imf[key] = dm.dmarray(outdata[key])
# Write the IMF data to .dat file
imf.write('IMF_data.dat')
# Write the IMF data to .h5 file
outhdf = dm.SpaceData()
for key in outdata.keys():
outhdf[key] = dm.dmarray(outdata[key])
outhdf['time'].attrs['epoch'] = t0.isoformat()
outhdf.toHDF5('advected.h5')
dest='fname',
default='IMF_ev5.dat',
help='Input SWMF IMF filename. Default "IMF_ev5.dat"')
parser.add_argument('-p',
'--path',
dest='path',
default='SWMF_inputs',
help='Path for input/output')
options = parser.parse_args()
np.random.seed(options.seed) #set seed for repeatability
#read SWMF ImfInput file
infilename = os.path.join(options.path, options.fname)
if os.path.isfile(infilename):
eventIMF = swmf.ImfInput(filename=infilename)
else:
raise IOError(
'Specified input file does not appear to exist or is not readable')
Ntimes = len(eventIMF['ux']) #3*1440 #N days at 1-min resolution
generateInputs = True
saveErrors = False
varlist = ['Vx_GSE', 'Bz_GSM', 'By_GSM']
Nvars = len(varlist)
map_dict = {'Vx_GSE': 'ux', 'Bz_GSM': 'bz', 'By_GSM': 'by'}
ylimdict = {
'Vx_GSE': [-300, -800],
'Bz_GSM': [-20, 20],
'By_GSM': [-20, 20]
