def interpolate(self, new_mlat_grid, new_mlt_grid ,method='nearest'):
"""
Rectangularize and Interpolate (using Linear 2D interpolation)
"""
X0, Y0 = satplottools.latlt2cart(self.mlat_orig.flatten(), self.mlt_orig.flatten(),self.hemisphere)
X, Y = satplottools.latlt2cart(new_mlat_grid.flatten(), new_mlt_grid.flatten(),self.hemisphere)
interpd_zvar = interpolate.griddata((X0,Y0), self.zvar.flatten(), (X,Y), method=method, fill_value=0.)
return interpd_zvar.reshape(new_mlat_grid.shape)
def draw_conductance(dt, startdt, enddt, hemi):
"""
Get the hall and pedersen conductance for one date and hemisphere
"""
estimator = ovation_prime.ConductanceEstimator(startdt, enddt)
mlatgrid, mltgrid, pedgrid, hallgrid = estimator.get_conductance(
dt, hemi=hemi, auroral=True, solar=True)
f = pp.figure(figsize=(11, 5))
aH = f.add_subplot(121)
aP = f.add_subplot(122)
X, Y = satplottools.latlt2cart(mlatgrid.flatten(), mltgrid.flatten(), hemi)
X = X.reshape(mlatgrid.shape)
Y = Y.reshape(mltgrid.shape)
satplottools.draw_dialplot(aH)
satplottools.draw_dialplot(aP)
mappableH = aH.pcolormesh(X, Y, hallgrid, vmin=0., vmax=20.)
mappableP = aP.pcolormesh(X, Y, pedgrid, vmin=0., vmax=15.)
aH.set_title("Hall Conductance")
aP.set_title("Pedersen Conductance")
f.colorbar(mappableH, ax=aH)
f.colorbar(mappableP, ax=aP)
f.suptitle(
"OvationPyme Conductance Output {0} Hemisphere at {1} \n".format(
hemi, dt.strftime('%c')),
fontweight='bold')
return f
def draw_weighted_flux(dt, atype='diff', jtype='electron energy flux'):
"""
Test automatic generation of omni_intervals in ovation_utilities
also by not specifying a start and end time for the FluxEstimator
"""
estimator = ovation_prime.FluxEstimator(atype, jtype)
mlatgridN, mltgridN, fluxgridN = estimator.get_flux_for_time(dt, hemi='N')
mlatgridS, mltgridS, fluxgridS = estimator.get_flux_for_time(dt, hemi='S')
f = pp.figure(figsize=(11, 5))
aN = f.add_subplot(121)
aS = f.add_subplot(122)
XN, YN = satplottools.latlt2cart(mlatgridN.flatten(), mltgridN.flatten(),
'N')
XS, YS = satplottools.latlt2cart(mlatgridS.flatten(), mltgridS.flatten(),
'S')
XN = XN.reshape(mlatgridN.shape)
YN = YN.reshape(mltgridN.shape)
XS = XS.reshape(mlatgridS.shape)
YS = YS.reshape(mltgridS.shape)
satplottools.draw_dialplot(aN)
satplottools.draw_dialplot(aS)
mappableN = aN.pcolormesh(XN, YN, fluxgridN, vmin=0, vmax=2)
mappableS = aS.pcolormesh(XS, YS, fluxgridS, vmin=0, vmax=2)
#aN.set_title("Northern Hemisphere Flux")
#aS.set_title("Southern Hemisphere Flux")
f.colorbar(mappableN, ax=aN)
f.colorbar(mappableS, ax=aS)
f.suptitle(
"OvationPyme Auroral Model Flux Output at {0} \n AuroralType:{1}, FluxType:{2}"
.format(dt.strftime('%c'), atype, jtype),
fontweight='bold')
return f
def draw_interpolated_conductance(new_mlat_grid, new_mlt_grid, dt, startdt,
enddt, hemi):
"""
Interpolate hall and pedersen conductance
onto grid described by mlat_grid, mlt_grid
"""
estimator = ovation_prime.ConductanceEstimator(startdt, enddt)
mlatgrid, mltgrid, pedgrid, hallgrid = estimator.get_conductance(
dt, hemi=hemi, auroral=True, solar=True)
ped_interpolator = ovation_prime.LatLocaltimeInterpolator(
mlatgrid, mltgrid, pedgrid)
new_pedgrid = ped_interpolator.interpolate(new_mlat_grid, new_mlt_grid)
hall_interpolator = ovation_prime.LatLocaltimeInterpolator(
mlatgrid, mltgrid, hallgrid)
new_hallgrid = hall_interpolator.interpolate(new_mlat_grid, new_mlt_grid)
f = pp.figure(figsize=(11, 5))
aH = f.add_subplot(121)
aP = f.add_subplot(122)
X, Y = satplottools.latlt2cart(new_mlat_grid.flatten(),
new_mlt_grid.flatten(), hemi)
X = X.reshape(new_mlat_grid.shape)
Y = Y.reshape(new_mlt_grid.shape)
satplottools.draw_dialplot(aH)
satplottools.draw_dialplot(aP)
mappableH = aH.pcolormesh(X, Y, new_hallgrid, vmin=0., vmax=20.)
mappableP = aP.pcolormesh(X, Y, new_pedgrid, vmin=0., vmax=15.)
aH.set_title("Hall Conductance")
aP.set_title("Pedersen Conductance")
f.colorbar(mappableH, ax=aH)
f.colorbar(mappableP, ax=aP)
f.suptitle(
"OvationPyme Interpolated Conductance {0} Hemisphere at {1}".format(
hemi, dt.strftime('%c')),
fontweight='bold')
return f
def draw_seasonal_flux(seasonN='summer',
seasonS='winter',
atype='diff',
jtype='electron energy flux',
dF=2134.17):
dF = 2134.17
estimatorN = ovation_prime.SeasonalFluxEstimator(seasonN, atype, jtype)
estimatorS = ovation_prime.SeasonalFluxEstimator(seasonS, atype, jtype)
fluxtupleN = estimatorN.get_gridded_flux(dF, combined_N_and_S=False)
(mlatgridN, mltgridN, fluxgridN) = fluxtupleN[:3]
fluxtupleS = estimatorS.get_gridded_flux(dF, combined_N_and_S=False)
(mlatgridS, mltgridS, fluxgridS) = fluxtupleS[3:]
f = pp.figure(figsize=(11, 5))
aN = f.add_subplot(121)
aS = f.add_subplot(122)
f2 = pp.figure(figsize=(5, 5))
a2 = f2.add_subplot(111)
XN, YN = satplottools.latlt2cart(mlatgridN.flatten(), mltgridN.flatten(),
'N')
XS, YS = satplottools.latlt2cart(mlatgridS.flatten(), mltgridS.flatten(),
'S')
XN = XN.reshape(mlatgridN.shape)
YN = YN.reshape(mltgridN.shape)
XS = XS.reshape(mlatgridS.shape)
YS = YS.reshape(mltgridS.shape)
satplottools.draw_dialplot(aN)
satplottools.draw_dialplot(aS)
satplottools.draw_dialplot(a2)
mappableN = aN.pcolormesh(XN, YN, fluxgridN, vmin=0, vmax=2)
mappableS = aS.pcolormesh(XS, YS, fluxgridS, vmin=0, vmax=2)
mappableNS = a2.pcolormesh(XN,
YN, (fluxgridS + fluxgridN) / 2,
vmin=0,
vmax=2)
#aN.set_title("Northern Hemisphere Flux")
#aS.set_title("Southern Hemisphere Flux")
f.colorbar(mappableN, ax=aN)
f.colorbar(mappableS, ax=aS)
f.colorbar(mappableNS, ax=a2)
f.suptitle(
"OvationPyme Auroral Model Raw Flux Output \n Season:{0}, AuroralType:{1}, FluxType:{2}, Newell Coupling:{3:.3f}"
.format(seasonN, atype, jtype, dF),
fontweight='bold')
f2.suptitle(
"OvationPyme Combined Hemisphere Output \n Season:{0}, AuroralType:{1}, FluxType:{2}, Newell Coupling:{3:.3f}"
.format(seasonN, atype, jtype, dF),
fontweight='bold')
return f, f2
def plot(self):
""" Draw a dialplot of the data
"""
f = pp.figure(figsize=(8.5, 11), dpi=300)
a = f.add_subplot(3, 1, 1)
a2 = f.add_subplot(3, 1, 2)
a3 = f.add_subplot(3, 1, 3)
iflx = self['intflux']
iflx[iflx == 0.] = .1
ms = 2
#Plot the track
X, Y = satplottools.latlt2cart(self['mlat'], self['mlt'], self.hemi)
a.plot(X, Y, 'k.', markersize=ms)
mappable = satplottools.hairplot(a,
self['mlat'],
self['mlt'],
np.log10(self['intflux']),
self.hemi,
vmin=np.log10(self.FLUX_MIN),
vmax=12)
f.colorbar(mappable,
label='log$_{10}$(Smoothed Integrated EEFlux)',
ax=a)
titlstr = "%s\n" % (os.path.split(self.satday.cdffn)[1])
a.text(0, 0, self.hemi)
#def dmsp_spectrogram( times, flux, channel_energies, lat=None, lt=None, fluxunits='eV/cm^2-s-sr-eV',
# logy=True, datalabel=None, cblims=None, title=None, ax=None, ax_cb=None ):
fluxstd = self.moving_average(self['total_flux_std'], 15)
a2.plot(self['uts'],
self['intflux'],
'k.',
label='Smoothed Int >1KeV Flx',
ms=5.)
a2.plot(self['uts'], fluxstd, 'r.', ms=3, label='Relative Uncertainty')
#a2.axhline(np.nanmean(fluxstd)-.5*np.nanstd(fluxstd),label='Mean Uncertainty',color='orange')
#a2.plot(self['uts'],self['intflux']-3*self['intflux']*fluxstd,'g.',ms=5,label='3*std lower bound')
a2.set_xlabel("UT Second of Day")
a2.set_yscale('log')
a2.axhline(self.FLUX_MIN, label='Threshold', color='grey')
a2_title = "Integrated Flux (9 Highest E Channels %.2feV-%.2feV)" % (
self['channel_energies'][0], self['channel_energies'][8])
a2.set_title(a2_title, fontsize="medium")
dmsp_spectrogram.dmsp_spectrogram(self['time'],
self['diff_flux'],
self['channel_energies'],
lat=self['mlat'],
lt=self['mlt'],
ax=a3,
cblims=[1e5, 1e10])
#a3.set_ylim([1e2,1e5])
if self.segments is not None:
for seg in self.segments:
a2.axvspan(seg['uts'][0],
seg['uts'][-1],
facecolor='m',
alpha=.2)
a2.grid(True)
a3.grid(True)
lw = 1.5
txtshift = 5
#Add boundaries
if self.idx_equator1 is not None:
a.plot(X[self.idx_equator1], Y[self.idx_equator1], 'bo', alpha=.5)
a2.axvline(self['uts'][self.idx_equator1],
color='b',
label='EQ1',
lw=lw)
a3.axvline(self['time'][self.idx_equator1],
color='b',
label='EQ1',
lw=lw)
a.text(X[self.idx_equator1],
Y[self.idx_equator1] - txtshift,
"EQ1",
color='b',
horizontalalignment='left')
else:
titlstr += 'No 1st EQB,'
if self.idx_pole1 is not None:
a.plot(X[self.idx_pole1], Y[self.idx_pole1], 'ro', alpha=.5)
a2.axvline(self['uts'][self.idx_pole1],
color='r',
label='PO1',
lw=lw)
a3.axvline(self['time'][self.idx_pole1],
color='r',
label='PO1',
lw=lw)
a.text(X[self.idx_pole1],
Y[self.idx_pole1] - txtshift,
"PO1",
color='r',
horizontalalignment='right')
else:
titlstr += 'No 1st PWB,'
if self.idx_pole2 is not None:
a.plot(X[self.idx_pole2],
Y[self.idx_pole2],
'o',
color='m',
alpha=.5)
a2.axvline(self['uts'][self.idx_pole2],
color='m',
label='PO2',
lw=lw)
a3.axvline(self['time'][self.idx_pole2],
color='m',
label='PO2',
lw=lw)
a.text(X[self.idx_pole2],
Y[self.idx_pole2] - txtshift,
"PO2",
color='m',
horizontalalignment='left')
else:
titlstr += 'No 2nd PWB,'
if self.idx_equator2 is not None:
a.plot(X[self.idx_equator2],
Y[self.idx_equator2],
'o',
color='deepskyblue',
alpha=.5)
a2.axvline(self['uts'][self.idx_equator2],
color='deepskyblue',
label='EQ2',
lw=lw)
a3.axvline(self['time'][self.idx_equator2],
color='deepskyblue',
label='EQ2',
lw=lw)
a.text(X[self.idx_equator2],
Y[self.idx_equator2] - txtshift,
"EQ2",
color='deepskyblue',
horizontalalignment='right')
else:
titlstr += 'No 2nd EQB,'
if titlstr[-1] == ',':
titlstr = titlstr[:-1] + '\n' # Remove trailing comma, add newline
#a.legend(ncol=2, fontsize="medium")
a2.legend(loc=0, fontsize="medium")
if self.failure_reason is not None:
a.text(-40, -60, self.failure_reason, color='red')
else:
desc = "Boundary 1: %.3f-%.3f, A: %.1e, RelUncertA: %.1f, A/A_max: %.3f, twidth: %.3fs\n" % (
self['mlat'][self.idx_equator1], self['mlat'][self.idx_pole1],
self.segment1.area, self.segment1.area_uncert,
self.segment1.area / self.max_seg_area, self.segment1.twidth)
desc += "Boundary 2: %.3f-%.3f,A: %.1e, RelUncertA: %.1f, A/A_max: %.3f, twidth: %.3fs\n" % (
self['mlat'][self.idx_equator2], self['mlat'][self.idx_pole2],
self.segment2.area, self.segment2.area_uncert,
self.segment2.area / self.max_seg_area, self.segment2.twidth)
desc += "Polar Cap Width in Time %.1f sec" % (
self.segment2['uts'][0] - self.segment1['uts'][-1])
a.text(-60., -60., desc, color='blue', fontsize="x-small")
if self.max_fom is not None:
titlstr += 'Identification FOM ( < 1.8 is questionable ): %.2f' % (
self.max_fom)
f.suptitle(titlstr, fontsize="medium")
#f.autofmt_xdate()
#f.tight_layout()
f.subplots_adjust(top=.94, hspace=.3, left=.15)
return f
def plot_orbit_cond(sat, year, month, day, orbit, minlat=50., plotdir=None):
#
#Prepare Plots
#
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
gs = gridspec.GridSpec(4, 11)
f = pp.figure()
#a0 = f.add_subplot(511)
#a05 = f.add_subplot(512)
split = 9
cbwidth = 1
a1 = pp.subplot(gs[0, :split])
a11 = pp.subplot(gs[0, split:split + cbwidth])
a2 = pp.subplot(gs[1, :split])
a22 = pp.subplot(gs[1:2, split:])
a3 = pp.subplot(gs[2, :split])
#a33 = pp.subplot(gs[2,split:])
a4 = pp.subplot(gs[3, :split])
a44 = pp.subplot(gs[3, split:split + cbwidth])
#
# Get conductance CDF
#
cdffn = get_cdf(sat,
year,
month,
day,
'ssj',
cdfdir=cdfdir,
return_file=True)
cdffn_cond = get_cond_cdffn(cdffn)
cdffn_cond_leaf = os.path.split(cdffn_cond)[-1]
cdffn_cond_leaf = os.path.splitext(cdffn_cond_leaf)[0]
hemi = 'N' if np.sign(orbit) == 1 else 'S'
#
# Figure Filename
#
if plotdir is None:
plotdir = '/home/liamk/code/glowcond/%s' % (cdffn_cond_leaf)
if not os.path.exists(plotdir):
os.makedirs(plotdir)
figfn = os.path.join(
plotdir, '%s_%s_%d.png' % (cdffn_cond_leaf, hemi, np.abs(orbit)))
with pycdf.CDF(cdffn_cond) as cdf:
orbit_index = cdf['ORBIT_INDEX'][:].flatten()
mlats = cdf['SC_APEX_LAT'][:].flatten()
in_orbit = orbit_index == orbit
subset = np.logical_and(in_orbit, np.abs(mlats) > minlat)
dts = cdf['Epoch'][:][subset]
uts = special_datetime.datetimearr2sod(
cdf['Epoch'][:]).flatten()[subset]
hod = uts / 3600.
auroral_region = cdf['AURORAL_REGION'][:].flatten()[subset]
chen = cdf['CHANNEL_ENERGIES'][:]
glats = cdf['SC_GEOCENTRIC_LAT'][:].flatten()[subset]
glons = cdf['SC_GEOCENTRIC_LON'][:].flatten()[subset]
mlats = cdf['SC_APEX_LAT'][:].flatten()[subset]
mlts = cdf['SC_APEX_MLT'][:].flatten()[subset]
eflux = cdf['ELE_DIFF_ENERGY_FLUX'][:][subset, :]
avg_energy = cdf['ELE_AVG_ENERGY'][:].flatten()[subset]
total_eflux = cdf['ELE_TOTAL_ENERGY_FLUX'][:].flatten()[subset]
total_eflux *= 1.6e-12 #eV/cm/s/sr -> mW/m^2
eflux *= 1.6e-12
z = cdf['CONDUCTIVITY_ALTITUDES'][:]
ped = cdf['PEDERSEN_CONDUCTIVITY'][:][subset]
hall = cdf['HALL_CONDUCTIVITY'][:][subset]
intped = cdf['PEDERSEN_CONDUCTANCE'][:][subset]
inthall = cdf['HALL_CONDUCTANCE'][:][subset]
if spectrograms:
#Plot Electron energy flux
dmsp_spectrogram.dmsp_spectrogram(
hod,
eflux,
chen,
datalabel=None,
cblims=[1e-7, 1e-2],
ax=a1,
ax_cb=a11,
fluxunits='Electron\nEnergy Flux\n[mW/m^2]')
#Plot Pedersen Conductance
a2.plot(hod, intped, 'r.-', label='DMSP+GLOW')
#a2.plot(uts[:-1],np.diff(inthall),label='dPed')
#a2.set_ylim([0,120])
#a2.set_ylim([0,1])
a2.legend(ncol=2, loc=0)
a2.set_ylabel('Pedersen\n Conductance\n[S]')
satplottools.draw_dialplot(a22)
x, y = satplottools.latlt2cart(mlats, mlts, hemi)
a22.plot(x, y, 'k.')
a22.text(x[-1], y[-1], 'End')
#Plot Hall Conductance
a3.plot(hod, inthall, 'r.-', label='DMSP+GLOW')
a3.legend(ncol=2, loc=0)
a3.set_ylabel('Hall\n Conductance\n[S]')
#Draw conductivity as a pcolor plot with log-scaled color scale
try:
ped[ped <= 0.] = 0.01
T, Z = np.meshgrid(hod, z)
mappable = a4.pcolor(T,
Z,
ped.T,
norm=LogNorm(vmin=np.nanmin(ped),
vmax=np.nanmax(ped)),
cmap='jet')
cb = pp.colorbar(mappable, cax=a44)
cb.ax.set_ylabel('Pedersen\nConductivity\n [S/m]')
a4.set_ylabel('Altitude\n[km]')
except:
pass
for ax in [a1, a2, a3]:
ax.xaxis.set_ticklabels([])
for ax in [a1, a2, a3, a4]:
ax.set_xlim(hod[0], hod[-1])
a4.set_xlabel('Hour of Day')
f.suptitle('%.2d-%2.d-%d %s orbit %d' %
(year, month, day, hemi, orbit))
f.savefig(figfn)
pp.close(f)
cblims=[1e-7, 1e-2],
ax=a1,
ax_cb=a11,
fluxunits='Electron\nEnergy Flux\n[mW/m^2]')
#Plot Pedersen Conductance
a2.plot(uts, intped, 'r.-', label='DMSP+GLOW')
a2.plot(uts, cond_ped_op, 'b.-', label='OP2010+Robinson')
#a2.plot(uts[:-1],np.diff(inthall),label='dPed')
#a2.set_ylim([0,120])
#a2.set_ylim([0,1])
a2.legend(ncol=2, loc=0)
a2.set_ylabel('Pedersen\n Conductance\n[S]')
satplottools.draw_dialplot(a22)
x, y = satplottools.latlt2cart(mlats, mlts, 'N')
a22.plot(x, y, 'k.')
a22.text(x[-1], y[-1], 'End')
#Plot Hall Conductance
a3.plot(uts, inthall, 'r.-', label='DMSP+GLOW')
a3.plot(uts, cond_hall_op, 'b.-', label='OP2010+Robinson')
a3.legend(ncol=2, loc=0)
a3.set_ylabel('Hall\n Conductance\n[S]')
#Draw conductivity as a pcolor plot with log-scaled color scale
T, Z = np.meshgrid(uts, z)
mappable = a4.pcolor(T,
Z,
ped.T,
norm=LogNorm(vmin=np.nanmin(ped),
