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_interpol_baddata(self):
"""interpol should give known results in presence of fill values"""
ans = array([ 0.5, 1.5, 2.5, 3.5, 4.5])
x = numpy.arange(10)
y = numpy.arange(10)
numpy.testing.assert_equal(ans, tb.interpol(numpy.arange(5)+0.5, x, y))
# now test with baddata
ans = numpy.ma.masked_array([0.5, 1.5, 2.5, 3.5, 4.5],
mask = [False, True, True, False, False], fill_value = 1e+20)
numpy.testing.assert_equal(ans, tb.interpol(numpy.arange(5)+0.5, x, y, baddata=2))
#test with baddata at end of array
ans = array([1.0, 9.0])
numpy.testing.assert_equal(ans, tb.interpol([-1,12], x, y, baddata=0))
def test_interpol_baddata(self):
"""interpol should give known results in presence of fill values"""
ans = array([ 0.5, 1.5, 2.5, 3.5, 4.5])
x = numpy.arange(10)
y = numpy.arange(10)
numpy.testing.assert_equal(ans, tb.interpol(numpy.arange(5)+0.5, x, y))
# now test with baddata
ans = numpy.ma.masked_array([0.5, 1.5, 2.5, 3.5, 4.5],
mask = [False, True, True, False, False], fill_value = 1e+20)
numpy.testing.assert_equal(ans, tb.interpol(numpy.arange(5)+0.5, x, y, baddata=2))
#test with baddata at end of array
ans = array([1.0, 9.0])
numpy.testing.assert_equal(ans, tb.interpol([-1,12], x, y, baddata=0))
def test_interpol_arb(self):
"""Interpol should give known results for arbitrary float/int wrapping"""
# test wrap arb
y = list(range(14))*2
x = list(range(len(y)))
real_ans = [1.5, 10.5, 13.5]
numpy.testing.assert_almost_equal(real_ans, tb.interpol([1.5, 10.5, 13.5], x, y, wrap=14).compressed())
real_ans = [1.5, 10.5, 1.5]
numpy.testing.assert_almost_equal(real_ans, tb.interpol([1.5, 10.5, 15.5], x, y)) # as a regression don't need wrap
# and test wrap with a float
real_ans = [1.5, 10.5, 13.5]
numpy.testing.assert_almost_equal(real_ans, tb.interpol([1.5, 10.5, 13.5], x, y, wrap=14.0).compressed())
# test wrap lon using 360.0 as input
y = list(range(360))*2
x = list(range(len(y)))
real_ans = numpy.ma.masked_array([1.5, 10.5, 359.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 359.5], x, y, wrap=360.0))
def test_interpol_arb(self):
"""Interpol should give known results for arbitrary float/int wrapping"""
# test wrap arb
y = list(range(14))*2
x = list(range(len(y)))
real_ans = [1.5, 10.5, 13.5]
numpy.testing.assert_almost_equal(real_ans, tb.interpol([1.5, 10.5, 13.5], x, y, wrap=14).compressed())
real_ans = [1.5, 10.5, 1.5]
numpy.testing.assert_almost_equal(real_ans, tb.interpol([1.5, 10.5, 15.5], x, y)) # as a regression don't need wrap
# and test wrap with a float
real_ans = [1.5, 10.5, 13.5]
numpy.testing.assert_almost_equal(real_ans, tb.interpol([1.5, 10.5, 13.5], x, y, wrap=14.0).compressed())
# test wrap lon using 360.0 as input
y = list(range(360))*2
x = list(range(len(y)))
real_ans = numpy.ma.masked_array([1.5, 10.5, 359.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 359.5], x, y, wrap=360.0))
def test_interpol_keywords(self):
"""Interpol should give known results with hour and lon keyword wrapping"""
# test wrap hour
y = list(range(24))*2
x = list(range(len(y)))
real_ans = numpy.ma.masked_array([1.5, 10.5, 23.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 23.5], x, y, wrap='hour'))
real_ans = numpy.ma.masked_array([1.5, 10.5, 1.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 1.5], x, y)) # as a regression don't need wrap
# test wrap lon
y = list(range(360))*2
x = list(range(len(y)))
real_ans = numpy.ma.masked_array([1.5, 10.5, 359.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 359.5], x, y, wrap='lon'))
real_ans = numpy.ma.masked_array([1.5, 10.5, 10.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 370.5], x, y)) # as a regression don't need wrap
def test_interpol_keywords(self):
"""Interpol should give known results with hour and lon keyword wrapping"""
# test wrap hour
y = list(range(24))*2
x = list(range(len(y)))
real_ans = numpy.ma.masked_array([1.5, 10.5, 23.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 23.5], x, y, wrap='hour'))
real_ans = numpy.ma.masked_array([1.5, 10.5, 1.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 1.5], x, y)) # as a regression don't need wrap
# test wrap lon
y = list(range(360))*2
x = list(range(len(y)))
real_ans = numpy.ma.masked_array([1.5, 10.5, 359.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 359.5], x, y, wrap='lon'))
real_ans = numpy.ma.masked_array([1.5, 10.5, 10.5],
mask = False, fill_value = 1e+20)
numpy.testing.assert_equal(real_ans, tb.interpol([1.5, 10.5, 370.5], x, y)) # as a regression don't need wrap
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 FIRE_Context_L1_L2(datafile, ephemfile):
full_data = dm.readJSONheadedASCII(datafile)
ephem = dm.readJSONheadedASCII(ephemfile)
meta = dm.readJSONheadedASCII(ephemfile)
data = Trim_data_file(full_data, ephem)
labels = ephem.keys()
ephem_fields = test_ephem_list
dt = spt.Ticktock(data['Epoch']).TAI
et = spt.Ticktock(ephem['DateTime']).TAI
for i in range(len(ephem_fields)):
dim = np.size(ephem[ephem_fields[i]][0])
print ephem_fields[i], dim
nx = np.empty([len(dt),dim])
if dim > 1:
for j in range(dim):
y = ephem[ephem_fields[i]][:,j]
nx[:,j] = tb.interpol(dt, et, y)
data[ephem_fields[i]] = dm.dmarray(nx, attrs = meta[ephem_fields[i]].attrs)
else:
y = ephem[ephem_fields[i]]
nx = tb.interpol(dt, et, y)
data[ephem_fields[i]] = dm.dmarray(nx, attrs = meta[ephem_fields[i]].attrs)
col = deepcopy(data['Context'][:,0])
sur = deepcopy(data['Context'][:,1])
despike(col, 250, 10)
despike(sur, 250, 10)
col = (col/(6*9))*1033
sur = (sur/(6*23))*772
data['col'] = dm.dmarray(col, attrs = {'Description': 'Collimated Detector Energy Flux', 'SCALE_TYPE': 'log'})
data['sur'] = dm.dmarray(sur, attrs = {'Description': 'Surface Detector Energy Flux', 'SCALE_TYPE': 'log'})
day = ephem['DateTime'][0][0:10]
outfile = datafile[:-25] + day + '-Context_L2.txt'
order = ['Epoch', 'col', 'sur', 'Context']
# order = ['Epoch', 'Context']
order.extend(ephem_fields)
dm.toJSONheadedASCII(outfile, data)
return data
