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 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_dmcopy(self):
"""dmcopy should copy datamodel objects"""
a = dm.SpaceData()
a[1] = dm.dmarray([1,2,3], attrs={1:1})
b = dm.dmcopy(a)
self.assertFalse(a is b) # they are not the same memory
np.testing.assert_almost_equal(a[1], b[1])
self.assertEqual(a[1].attrs, b[1].attrs)
b = dm.dmcopy(a[1])
np.testing.assert_almost_equal(a[1], b)
self.assertEqual(a[1].attrs, b.attrs)
a = np.arange(10)
b = dm.dmcopy(a)
np.testing.assert_almost_equal(a, b)
a = [1,2,3]
b = dm.dmcopy(a)
self.assertEqual(a, b)
def plotSpectrogram(self, ecol=0, **kwargs):
'''
Plot a spectrogram of the flux along the requested orbit, as a function of Lm and time
Other Parameters
----------------
zlim : list
2-element list with upper and lower bounds for color scale
colorbar_label : string
text to appear next to colorbar (default is 'Flux' plus the units)
ylabel : string
text to label y-axis (default is 'Lm' plus the field model name)
title : string
text to appear above spectrogram (default is climatology model name, data type and energy)
'''
import spacepy.plot as splot
if 'Lm' not in self:
self.getLm()
sd = dm.SpaceData()
sd['Lm'] = self['Lm']
#filter any bad Lm
goodidx = sd['Lm'] > 1
sd['Lm'] = sd['Lm'][goodidx]
Lm_lim = [2.0, 8.0]
#TODO: allow user-definition of bins in time and Lm
varname = self.attrs['varname']
sd['Epoch'] = dm.dmcopy(
self['Epoch'])[goodidx] #TODO: assumes 1 pitch angle, generalize
sd['1D_dataset'] = self[varname][
goodidx, ecol] #TODO: assumes 1 pitch angle, generalize
spec = splot.spectrogram(sd,
variables=['Epoch', 'Lm', '1D_dataset'],
ylim=Lm_lim)
if 'zlim' not in kwargs:
zmax = 10**(int(np.log10(max(sd['1D_dataset']))) + 1)
idx = np.logical_and(sd['1D_dataset'] > 0, sd['Lm'] > Lm_lim[0])
idx = np.logical_and(idx, sd['Lm'] <= Lm_lim[1])
zmin = 10**int(np.log10(min(sd['1D_dataset'][idx])))
kwargs['zlim'] = [zmin, zmax]
if 'colorbar_label' not in kwargs:
flux_units = self[varname].attrs['UNITS']
kwargs['colorbar_label'] = '{0} ['.format(varname) + re.sub(
'(\^[\d|-]*)+', _grp2mathmode, flux_units) + ']'
if 'ylabel' not in kwargs:
kwargs['ylabel'] = 'L$_M$' + ' ' + '[{0}]'.format(
self['Lm'].attrs['MODEL'])
if 'title' not in kwargs:
kwargs['title'] = '{model_type} {varname}: '.format(**self.attrs) + \
'{0} {1}'.format(self['Energy'][ecol], self['Energy'].attrs['UNITS'])
reset_shrink = splot.mpl.mathtext.SHRINK_FACTOR
splot.mpl.mathtext.SHRINK_FACTOR = 0.85
splot.mpl.mathtext.GROW_FACTOR = 1 / 0.85
ax = spec.plot(cmap='plasma', **kwargs)
splot.mpl.mathtext.SHRINK_FACTOR = reset_shrink
splot.mpl.mathtext.GROW_FACTOR = 1 / reset_shrink
return ax
def plotSpectrogram(self, ecol=0, **kwargs):
'''
Plot a spectrogram of the flux along the requested orbit, as a function of Lm and time
Other Parameters
----------------
zlim : list
2-element list with upper and lower bounds for color scale
colorbar_label : string
text to appear next to colorbar (default is 'Flux' plus the units)
ylabel : string
text to label y-axis (default is 'Lm' plus the field model name)
title : string
text to appear above spectrogram (default is climatology model name, data type and energy)
'''
import spacepy.plot as splot
if 'Lm' not in self:
self.getLm()
sd = dm.SpaceData()
sd['Lm'] = self['Lm']
#filter any bad Lm
goodidx = sd['Lm']>1
sd['Lm'] = sd['Lm'][goodidx]
Lm_lim = [2.0,8.0]
#TODO: allow user-definition of bins in time and Lm
varname = self.attrs['varname']
sd['Epoch'] = dm.dmcopy(self['Epoch'])[goodidx] #TODO: assumes 1 pitch angle, generalize
sd['1D_dataset'] = self[varname][goodidx,ecol] #TODO: assumes 1 pitch angle, generalize
spec = splot.spectrogram(sd, variables=['Epoch', 'Lm', '1D_dataset'], ylim=Lm_lim)
if 'zlim' not in kwargs:
zmax = 10**(int(np.log10(max(sd['1D_dataset'])))+1)
idx = np.logical_and(sd['1D_dataset']>0, sd['Lm']>Lm_lim[0])
idx = np.logical_and(idx, sd['Lm']<=Lm_lim[1])
zmin = 10**int(np.log10(min(sd['1D_dataset'][idx])))
kwargs['zlim'] = [zmin, zmax]
if 'colorbar_label' not in kwargs:
flux_units = self[varname].attrs['UNITS']
kwargs['colorbar_label'] = '{0} ['.format(varname) + re.sub('(\^[\d|-]*)+', _grp2mathmode, flux_units) + ']'
if 'ylabel' not in kwargs:
kwargs['ylabel'] = 'L$_M$'+' '+'[{0}]'.format(self['Lm'].attrs['MODEL'])
if 'title' not in kwargs:
kwargs['title'] = '{model_type} {varname}: '.format(**self.attrs) + \
'{0} {1}'.format(self['Energy'][ecol], self['Energy'].attrs['UNITS'])
reset_shrink = splot.mpl.mathtext.SHRINK_FACTOR
splot.mpl.mathtext.SHRINK_FACTOR = 0.85
splot.mpl.mathtext.GROW_FACTOR = 1/0.85
ax = spec.plot(cmap='plasma', **kwargs)
splot.mpl.mathtext.SHRINK_FACTOR = reset_shrink
splot.mpl.mathtext.GROW_FACTOR = 1/reset_shrink
return ax
except KeyError:
usestyle = lookdict['default']
try:
plt.style.use(usestyle)
except AttributeError: #plt.style.use not available, old matplotlib?
dum = matplotlib.rc_params_from_file(usestyle)
styapply = dict()
#remove None values as these seem to cause issues...
for key in dum:
if dum[key] is not None: styapply[key] = dum[key]
for key in styapply:
matplotlib.rcParams[key] = styapply[key]
matplotlib.rcParams['image.cmap'] = cmap
#save current rcParams before applying spacepy style
oldParams = dmcopy(matplotlib.rcParams)
style()
def revert_style():
import matplotlib
for key in oldParams:
matplotlib.rcParams[key] = oldParams[key]
def dual_half_circle(center=(0,0), radius=1.0,
sun_direction='right', ax=None, colors=('w','k'),
**kwargs):
"""
Plot two half circles to a plot with the specified face colors and
rotation. This is normal to use to denote the sun direction in
magnetospheric science plots.
def levelPlot(data,
var=None,
time=None,
levels=(3, 5),
target=None,
colors=None,
**kwargs):
"""
Draw a step-plot with up to 5 levels following a color cycle (e.g. Kp index "stoplight")
Parameters
----------
data : array-like, or dict-like
Data for plotting. If dict-like, the key providing an array-like
to plot must be given to var keyword argument.
Other Parameters
----------------
var : string
Name of key in dict-like input that contains data
time : array-like or string
Name of key in dict-like that contains time, or arraylike of datetimes
levels : array-like, up to 5 levels
Breaks between levels in data that should be shown as distinct colors
target : figure or axes
Target axes or figure window
colors : array-like
Colors to use for the color sequence (if insufficient colors, will use as a cycle)
**kwargs : other keywords
Other keywords to pass to spacepy.toolbox.binHisto
Returns
-------
binned : tuple
Tuple of the binned data and bins
Examples
--------
>>> import spacepy.plot as splot
>>> import spacepy.time as spt
>>> import spacepy.omni as om
>>> tt = spt.tickrange('2012/09/28','2012/10/2', 3/24.)
>>> omni = om.get_omni(tt)
>>> splot.levelPlot(omni, var='Kp', time='UTC', colors=['seagreen', 'orange', 'crimson'])
"""
#assume dict-like/key-access, before moving to array-like
if var is not None:
try:
usearr = data[var]
except KeyError:
raise KeyError('Key "{1}" not present in data'.format(var))
else:
#var is None, so make sure we don't have a dict-like
if not isinstance(data, Mapping):
usearr = np.asarray(data)
else:
raise TypeError(
'Data appears to be dict-like without a key being given')
tflag = False
if time is not None:
from scipy.stats import mode
try:
times = data[time]
except (KeyError, ValueError, IndexError):
times = time
try:
times = matplotlib.dates.date2num(times)
tflag = True
except AttributeError:
#the x-data are a non-datetime
times = np.asarray(time)
#now add the end-point
stepsize, dum = mode(np.diff(times), axis=None)
times = np.hstack([times, times[-1] + stepsize])
else:
times = np.asarray(range(0, len(usearr) + 1))
if not colors:
if len(levels) <= 3:
#traffic light colours that are distinct to protanopes and deuteranopes
colors = ['lime', 'yellow', 'crimson', 'saddlebrown']
else:
colors = matplotlib.rcParams['axes.color_cycle']
else:
try:
assert len(colors) > len(levels)
except AssertionError:
#cycle the given colors, if not enough are given
colors = list(colors) * int(1 + len(levels) / len(colors))
if 'alpha' not in kwargs:
kwargs['alpha'] = 0.75
if 'legend' not in kwargs:
legend = False
else:
legend = kwargs['legend']
del kwargs['legend']
fig, ax = set_target(target)
subset = np.asarray(dmcopy(usearr))
def fill_between_steps(ax, x, y1, **kwargs):
y2 = np.zeros_like(y1)
stepsxx = x.repeat(2)[1:-1]
stepsyy = y1.repeat(2)
y2 = np.zeros_like(stepsyy)
ax.fill_between(stepsxx, stepsyy, y2, **kwargs)
if mpl.__version__ < '1.5.0':
#pre-v1.5.0, need to manually add an artist for the legend
p = plt.Rectangle((0, 0), 0, 0, **kwargs)
ax.add_patch(p)
#below threshold 1
idx = 0
inds = usearr > levels[0]
subset[inds] = np.nan
kwargs['label'] = u'≤{0}'.format(levels[idx])
fill_between_steps(ax, times, subset, color=colors[0], zorder=30, **kwargs)
#for each of the "between" thresholds
for idx in range(1, len(levels)):
subset = np.asarray(dmcopy(usearr))
inds = np.bitwise_or(usearr <= levels[idx - 1], usearr > levels[idx])
subset[inds] = np.nan
kwargs['label'] = u'>{0},≤{1}'.format(levels[idx - 1], levels[idx])
fill_between_steps(ax,
times,
subset,
color=colors[idx],
zorder=30 - (idx * 2),
**kwargs)
#last
idx += 1
try:
inds = usearr <= levels[idx - 1]
subset = np.asarray(dmcopy(usearr))
subset[inds] = np.nan
kwargs['label'] = '>{0}'.format(levels[-1])
fill_between_steps(ax,
times,
subset,
color=colors[idx],
zorder=30 - (idx * 2),
**kwargs)
except:
pass
#if required, set x axis to times
if tflag:
try:
applySmartTimeTicks(ax, data[time])
except (IndexError, KeyError):
#using data array to index, so should just use time
applySmartTimeTicks(ax, time)
ax.grid('off',
which='minor') #minor grid usually looks bad on these...
if legend:
ncols = len(levels) + 1
if ncols > 3: ncols = ncols // 2
ax.legend(loc='upper left', ncol=ncols)
return ax
with warnings.catch_warnings():
warnings.simplefilter("ignore")
dum = mpl.rc_params_from_file(usestyle)
styapply = dict()
#remove None values as these seem to cause issues...
for key in dum:
if dum[key] is not None: styapply[key] = dum[key]
for key in styapply:
mpl.rcParams[key] = styapply[key]
mpl.rcParams['image.cmap'] = cmap
#save current rcParams before applying spacepy style
oldParams = dict()
for key, val in mpl.rcParams.items():
oldParams[key] = dmcopy(val)
if config['apply_plot_styles']:
style()
def revert_style():
'''Revert plot style settings to those in use prior to importing spacepy.plot
'''
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for key in oldParams:
try:
mpl.rcParams[key] = oldParams[key]
except ValueError:
pass
def plotSpectrogram(self, ecol=0, pvars=None, **kwargs):
'''
Plot a spectrogram of the flux along the requested orbit, as a function of Lm and time
Other Parameters
----------------
zlim : list
2-element list with upper and lower bounds for color scale
colorbar_label : string
text to appear next to colorbar (default is 'Flux' plus the units)
ylabel : string
text to label y-axis (default is 'Lm' plus the field model name)
title : string
text to appear above spectrogram (default is climatology model name, data type and energy)
pvars : list
list of plotting variable names in order [Epoch-like (X axis), Flux-like (Z axis), Energy (Index var for Flux-like)]
ylim : list
2-element list with upper and lower bounds for y axis
'''
import spacepy.plot as splot
if 'Lm' not in self:
self.getLm()
sd = dm.SpaceData()
if pvars is None:
varname = self.attrs['varname']
enname = 'Energy'
epvar = 'Epoch'
else:
epvar = pvars[0]
varname = pvars[1]
enname = pvars[2]
if len(self['Lm']) != len(self[epvar]): #Lm needs interpolating to new timebase
import matplotlib.dates as mpd
tmptimetarg, tmptimesrc = mpd.date2num(self[epvar]), mpd.date2num(self['Epoch'])
sd['Lm'] = dm.dmarray(np.interp(tmptimetarg, tmptimesrc, self['Lm'], left=np.nan, right=np.nan))
else:
sd['Lm'] = self['Lm']
#filter any bad Lm
goodidx = sd['Lm']>1
sd['Lm'] = sd['Lm'][goodidx]
if 'ylim' in kwargs:
Lm_lim = kwargs['ylim']
del kwargs['ylim']
else:
Lm_lim = [2.0,8.0]
#TODO: allow user-definition of bins in time and Lm
sd['Epoch'] = dm.dmcopy(self[epvar])[goodidx] #TODO: assumes 1 pitch angle, generalize
try:
sd['1D_dataset'] = self[varname][goodidx,ecol] #TODO: assumes 1 pitch angle, generalize
except IndexError: #1-D
sd['1D_dataset'] = self[varname][goodidx]
bins = [[],[]]
if 'tbins' not in kwargs:
bins[0] = spt.tickrange(self[epvar][0], self[epvar][-1], 3/24.).UTC
else:
bins[0] = kwargs['tbins']
del kwargs['tbins']
if 'Lbins' not in kwargs:
bins[1] = np.arange(Lm_lim[0], Lm_lim[1], 1./4.)
else:
bins[1] = kwargs['Lbins']
del kwargs['Lbins']
spec = splot.spectrogram(sd, variables=['Epoch', 'Lm', '1D_dataset'], ylim=Lm_lim, bins=bins)
if 'zlim' not in kwargs:
zmax = 10 ** (int(np.log10(max(sd['1D_dataset']))) + 1)
idx = np.logical_and(sd['1D_dataset'] > 0, sd['Lm'] > Lm_lim[0])
idx = np.logical_and(idx, sd['Lm'] <= Lm_lim[1])
zmin = 10 ** int(np.log10(min(sd['1D_dataset'][idx])))
kwargs['zlim'] = [zmin, zmax]
if 'colorbar_label' not in kwargs:
flux_units = self[varname].attrs['UNITS']
kwargs['colorbar_label'] = '{0} ['.format(varname) + re.sub('(\^[\d|-]*)+', _grp2mathmode, flux_units) + ']'
if 'ylabel' not in kwargs:
kwargs['ylabel'] = 'L$_M$' + ' ' + '[{0}]'.format(self['Lm'].attrs['MODEL'])
if 'title' not in kwargs:
kwargs['title'] = '{model_type} {varname}: '.format(**self.attrs) + \
'{0:5.2f} {1}'.format(self[enname][ecol], self[enname].attrs['UNITS'])
reset_shrink = splot.mpl.mathtext.SHRINK_FACTOR
splot.mpl.mathtext.SHRINK_FACTOR = 0.85
splot.mpl.mathtext.GROW_FACTOR = 1 / 0.85
ax = spec.plot(cmap='plasma', **kwargs)
splot.mpl.mathtext.SHRINK_FACTOR = reset_shrink
splot.mpl.mathtext.GROW_FACTOR = 1 / reset_shrink
return ax
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 plotSpectrogram(self, ecol=0, pvars=None, **kwargs):
'''
Plot a spectrogram of the flux along the requested orbit, as a function of Lm and time
Other Parameters
----------------
zlim : list
2-element list with upper and lower bounds for color scale
colorbar_label : string
text to appear next to colorbar (default is 'Flux' plus the units)
ylabel : string
text to label y-axis (default is 'Lm' plus the field model name)
title : string
text to appear above spectrogram (default is climatology model name, data type and energy)
pvars : list
list of plotting variable names in order [Epoch-like (X axis), Flux-like (Z axis), Energy (Index var for Flux-like)]
ylim : list
2-element list with upper and lower bounds for y axis
'''
import spacepy.plot as splot
if 'Lm' not in self:
self.getLm()
sd = dm.SpaceData()
if pvars is None:
varname = self.attrs['varname']
enname = 'Energy'
epvar = 'Epoch'
else:
epvar = pvars[0]
varname = pvars[1]
enname = pvars[2]
if len(self['Lm']) != len(
self[epvar]): #Lm needs interpolating to new timebase
import matplotlib.dates as mpd
tmptimetarg, tmptimesrc = mpd.date2num(self[epvar]), mpd.date2num(
self['Epoch'])
sd['Lm'] = dm.dmarray(
np.interp(tmptimetarg,
tmptimesrc,
self['Lm'],
left=np.nan,
right=np.nan))
else:
sd['Lm'] = self['Lm']
#filter any bad Lm
goodidx = sd['Lm'] > 1
sd['Lm'] = sd['Lm'][goodidx]
if 'ylim' in kwargs:
Lm_lim = kwargs['ylim']
del kwargs['ylim']
else:
Lm_lim = [2.0, 8.0]
#TODO: allow user-definition of bins in time and Lm
sd['Epoch'] = dm.dmcopy(
self[epvar])[goodidx] #TODO: assumes 1 pitch angle, generalize
try:
sd['1D_dataset'] = self[varname][
goodidx, ecol] #TODO: assumes 1 pitch angle, generalize
except IndexError: #1-D
sd['1D_dataset'] = self[varname][goodidx]
bins = [[], []]
if 'tbins' not in kwargs:
bins[0] = spt.tickrange(self[epvar][0], self[epvar][-1],
3 / 24.).UTC
else:
bins[0] = kwargs['tbins']
del kwargs['tbins']
if 'Lbins' not in kwargs:
bins[1] = np.arange(Lm_lim[0], Lm_lim[1], 1. / 4.)
else:
bins[1] = kwargs['Lbins']
del kwargs['Lbins']
spec = splot.spectrogram(sd,
variables=['Epoch', 'Lm', '1D_dataset'],
ylim=Lm_lim,
bins=bins)
if 'zlim' not in kwargs:
zmax = 10**(int(np.log10(max(sd['1D_dataset']))) + 1)
idx = np.logical_and(sd['1D_dataset'] > 0, sd['Lm'] > Lm_lim[0])
idx = np.logical_and(idx, sd['Lm'] <= Lm_lim[1])
zmin = 10**int(np.log10(min(sd['1D_dataset'][idx])))
kwargs['zlim'] = [zmin, zmax]
if 'colorbar_label' not in kwargs:
flux_units = self[varname].attrs['UNITS']
kwargs['colorbar_label'] = '{0} ['.format(varname) + re.sub(
'(\^[\d|-]*)+', _grp2mathmode, flux_units) + ']'
if 'ylabel' not in kwargs:
kwargs['ylabel'] = 'L$_M$' + ' ' + '[{0}]'.format(
self['Lm'].attrs['MODEL'])
if 'title' not in kwargs:
kwargs['title'] = '{model_type} {varname}: '.format(**self.attrs) + \
'{0:5.2f} {1}'.format(self[enname][ecol], self[enname].attrs['UNITS'])
reset_shrink = splot.mpl.mathtext.SHRINK_FACTOR
splot.mpl.mathtext.SHRINK_FACTOR = 0.85
splot.mpl.mathtext.GROW_FACTOR = 1 / 0.85
ax = spec.plot(cmap='plasma', **kwargs)
splot.mpl.mathtext.SHRINK_FACTOR = reset_shrink
splot.mpl.mathtext.GROW_FACTOR = 1 / reset_shrink
return ax
def singleRunEwithEnsemble(runname,
ensname,
searchpatt='1_geoe_*.csv',
outdir='E_maps',
eVar='Estd'):
'''
Optional Parameters
------------------
eVar : string
Estd or Emag
'''
import cartopy.feature as cfea
import cartopy.feature.nightshade as night
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
useProject = ccrs.PlateCarree #Projection to use for plotting, e.g., ccrs.AlbersEqualArea
plotprojection = useProject(central_longitude=-90.0)
pdata = {
'plotvar': 'Emag',
'plotvec': 'E',
'dataprojection': ccrs.PlateCarree(),
'plotprojection': plotprojection,
}
doQuiver = False #may want to change this in future, so I'm leaving the code in
#add features from ArcGIS shapefile?
pdata['shapes'] = None
rundir = runname[4:]
globterm = os.path.join(runname, searchpatt)
allfiles = sorted(glob.glob(globterm))
#downselect files to use
#TODO: remove hardcoded downselect of timerange
allfiles = [
fn for fn in allfiles
if (int(re.search('\d{8}-(\d{6})', fn).groups()[0]) >= 81500) and (
int(re.search('\d{8}-(\d{6})', fn).groups()[0]) <= 84500)
]
infiles = [
fn for fn in allfiles if re.search('\d{8}-(\d{6})', fn).groups()[0]
[-2:] in ['00', '15', '30', '45']
]
#get ensemble member directories
members = [en for en in glob.glob(ensname) if en != runname]
print('Using {0} ensemble members'.format(len(members)))
for mm in members:
print('{0}'.format(mm))
#loop over files and plot map of |E|
for fname in infiles:
edata = gmdtools.readEcsv(fname)
#find matching file for each ens. member
filepart = os.path.split(fname)[-1]
collect = [edata]
for mdir in members:
fname = os.path.join(mdir, filepart)
try:
collect.append(gmdtools.readEcsv(fname))
except:
print('Hit an issue with {0}'.format(fname))
continue
#collect Emag and calculate stadard deviation across ensemble members at each gridpoint
combinedMag = np.dstack(cc['Emag'] for cc in collect)
combinedEast = np.dstack(cc['Ee'] for cc in collect)
combinedNorth = np.dstack(cc['En'] for cc in collect)
ensmean = dm.dmcopy(edata)
ensmean['Ee_raw'] = np.stack(cc['Ee_raw']
for cc in collect).mean(axis=0)
ensmean['En_raw'] = np.stack(cc['En_raw']
for cc in collect).mean(axis=0)
ensmean['Estd'] = combinedMag.std(axis=-1)
ensmean['Ee_ensMean'] = combinedEast.mean(axis=-1)
ensmean['En_ensMean'] = combinedNorth.mean(axis=-1)
ensmean['Emag'] = np.sqrt(ensmean['Ee_ensMean']**2 +
ensmean['En_ensMean']**2)
#
print('Working on timestep {0}. # members = {1}'.format(
edata.attrs['time'], len(collect)))
#
fig = plt.figure(figsize=(10, 5.5))
#set up first map panel (reference run)
pdata['plotvar'] = 'Emag'
sstyle = {'color': 'black'}
qstyle = {
'color': 'darkgrey',
'pivot': 'mid',
'alpha': 0.6,
'scale': 1,
'scale_units': 'xy'
}
ax = plt.subplot(2, 1, 1, projection=pdata['plotprojection'])
ax.coastlines(color='darkgrey')
ax.add_feature(cfea.BORDERS, linestyle=':', edgecolor='darkgrey')
ax.add_feature(night.Nightshade(edata.attrs['time'], alpha=0.3))
lon_formatter = LongitudeFormatter(number_format='.1f',
degree_symbol='',
dateline_direction_label=True)
lat_formatter = LatitudeFormatter(number_format='.1f',
degree_symbol='')
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.set_extent([-165.0, 45.0, 30.0, 80.0], crs=ccrs.PlateCarree())
ax = gmdtools.plotFilledContours(edata, pdata, addTo=ax)
ax = gmdtools.plotVectors(edata,
pdata,
addTo=ax,
maxVec=2.75,
sstyle=sstyle)
ax.set_title('{0}'.format(edata.attrs['time'].isoformat()))
#set up second map panel (use requested (Emag_ens or Estd)
ax2 = plt.subplot(2, 1, 2, projection=pdata['plotprojection'])
ax2.coastlines(color='darkgrey')
ax2.add_feature(cfea.BORDERS, linestyle=':', edgecolor='darkgrey')
ax2.add_feature(night.Nightshade(edata.attrs['time'], alpha=0.3))
ax2.xaxis.set_major_formatter(lon_formatter)
ax2.yaxis.set_major_formatter(lat_formatter)
ax2.set_extent([-165.0, 45.0, 30.0, 80.0], crs=ccrs.PlateCarree())
pdata['plotvar'] = eVar
ax2 = gmdtools.plotFilledContours(ensmean, pdata, addTo=ax2)
#plot all ensemble members as light grey quivers
if doQuiver:
for ense in collect:
ax2 = gmdtools.plotVectors(ense,
pdata,
addTo=ax2,
quiver=True,
qstyle=qstyle)
qstyle['alpha'] = 1
qstyle['color'] = 'black'
#then plot ensemble mean with streamlines...
sstyle['color'] = 'darkblue'
#collect Emag and calculate [mean/standard deviation] across ensemble members at each gridpoint
ax2 = gmdtools.plotVectors(ensmean,
pdata,
addTo=ax2,
quiver=False,
maxVec=2.75,
qstyle=qstyle,
sstyle=sstyle)
for aa in [ax, ax2]:
aa.set_xticks([-150, -120, -90, -60, -30, 0, 30],
crs=ccrs.PlateCarree())
aa.set_yticks([35, 45, 55, 65, 75], crs=ccrs.PlateCarree())
#now annotate panels with "Reference" and "Ensemble" (put text at Lisbon, Portugal)
anchor = (-9.195, 38.744) #Lisbon #(-7.9304, 37.0194) #Faro, Port.
#ensText = 'Ensemble Mean + $\sigma(|E|)$' if eVar=='Estd' else 'Ensemble Mean'
ensText = 'Ensemble Mean'
ax.text(anchor[0],
anchor[1],
'Unperturbed',
verticalalignment='top',
weight='semibold',
bbox=dict(facecolor='white', alpha=0.3, edgecolor='None'),
transform=ccrs.PlateCarree())
ax2.text(anchor[0],
anchor[1],
ensText + '\n(N={0})'.format(len(collect)),
verticalalignment='top',
weight='semibold',
color='darkblue',
bbox=dict(facecolor='white', alpha=0.3, edgecolor='None'),
transform=ccrs.PlateCarree())
#windows can't handle colons in filenames...
isotime = edata.attrs['time'].isoformat()
plt.tight_layout()
plt.savefig(os.path.join(
outdir, r'{0}_{1}.png'.format(pdata['plotvar'],
isotime.replace(':', ''))),
dpi=300)
plt.close()
gmdtools.makeSymlinks(outdir, kind='E')
error_series[run_num,
it * blocksize:it * blocksize + blocksize,
vidx] = savedata[var][bidx:bidx + blocksize]
elif Ntimes - it * blocksize > 0:
room = len(error_series[run_num, it * blocksize:, vidx])
error_series[run_num, it * blocksize:,
vidx] = savedata[var][bidx:bidx + room]
else:
pass
#modify SWMF ImfInput and write new file
outfilename = '.'.join([
'_'.join([infilename.split('.')[0], '{0:03d}'.format(run_num)]),
'dat'
])
if generateInputs:
surrogateIMF = dm.dmcopy(eventIMF)
for vidx, var in enumerate(varlist):
surrogateIMF[map_dict[var]] += error_series[run_num, :Ntimes,
vidx]
#then write to file
surrogateIMF.write(outfilename)
#save error series if req'd
if saveErrors:
out = dm.SpaceData()
out['errors'] = dm.dmarray(error_series)
out['errors'].attrs['DEPEND_0'] = 'EnsembleNumber'
out['errors'].attrs['DEPEND_1'] = 'Timestep'
out['errors'].attrs['DEPEND_2'] = 'Variable'
out.toHDF5('MBB_errors.h5'.format(var))
def levelPlot(data, var=None, time=None, levels=(3, 5), target=None, colors=None, **kwargs):
"""
Draw a step-plot with up to 5 levels following a color cycle (e.g. Kp index "stoplight")
Parameters
----------
data : array-like, or dict-like
Data for plotting. If dict-like, the key providing an array-like
to plot must be given to var keyword argument.
Other Parameters
----------------
var : string
Name of key in dict-like input that contains data
time : array-like or string
Name of key in dict-like that contains time, or arraylike of datetimes
levels : array-like, up to 5 levels
Breaks between levels in data that should be shown as distinct colors
target : figure or axes
Target axes or figure window
colors : array-like
Colors to use for the color sequence (if insufficient colors, will use as a cycle)
**kwargs : other keywords
Other keywords to pass to spacepy.toolbox.binHisto
Returns
-------
binned : tuple
Tuple of the binned data and bins
Examples
--------
>>> import spacepy.plot as splot
>>> import spacepy.time as spt
>>> import spacepy.omni as om
>>> tt = spt.tickrange('2012/09/28','2012/10/2', 3/24.)
>>> omni = om.get_omni(tt)
>>> splot.levelPlot(omni, var='Kp', time='UTC', colors=['seagreen', 'orange', 'crimson'])
"""
#assume dict-like/key-access, before moving to array-like
if var is not None:
try:
usearr = data[var]
except KeyError:
raise KeyError('Key "{1}" not present in data'.format(var))
else:
#var is None, so make sure we don't have a dict-like
import collections
if not isinstance(data, collections.Mapping):
usearr = np.asarray(data)
else:
raise TypeError('Data appears to be dict-like without a key being given')
tflag = False
if time is not None:
from scipy.stats import mode
try:
times = data[time]
except (KeyError, ValueError, IndexError):
times = time
try:
times = matplotlib.dates.date2num(times)
tflag = True
except AttributeError:
#the x-data are a non-datetime
times = np.asarray(time)
#now add the end-point
stepsize, dum = mode(np.diff(times), axis=None)
times = np.hstack([times, times[-1]+stepsize])
else:
times = np.asarray(range(0, len(usearr)+1))
if not colors:
if len(levels)<=3:
#traffic light colours that are distinct to protanopes and deuteranopes
colors = ['lime', 'yellow', 'crimson', 'saddlebrown']
else:
colors = matplotlib.rcParams['axes.color_cycle']
else:
try:
assert len(colors) > len(levels)
except AssertionError:
#cycle the given colors, if not enough are given
colors = list(colors)*int(1+len(levels)/len(colors))
if 'alpha' not in kwargs:
kwargs['alpha']=0.75
if 'legend' not in kwargs:
legend = False
else:
legend = kwargs['legend']
del kwargs['legend']
fig, ax = set_target(target)
subset = np.asarray(dmcopy(usearr))
def fill_between_steps(ax, x, y1, **kwargs):
y2 = np.zeros_like(y1)
stepsxx = x.repeat(2)[1:-1]
stepsyy = y1.repeat(2)
y2 = np.zeros_like(stepsyy)
ax.fill_between(stepsxx, stepsyy, y2, **kwargs)
p = plt.Rectangle((0, 0), 0, 0, **kwargs)
ax.add_patch(p)
#below threshold 1
idx = 0
inds = usearr>levels[0]
subset[inds] = np.nan
kwargs['label'] = '<{0}'.format(levels[idx])
fill_between_steps(ax, times, subset, color=colors[0], zorder=30, **kwargs)
#for each of the "between" thresholds
for idx in range(1,len(levels)):
subset = np.asarray(dmcopy(usearr))
inds = np.bitwise_or(usearr<=levels[idx-1], usearr>levels[idx])
subset[inds] = np.nan
kwargs['label'] = '{0}-{1}'.format(levels[idx-1], levels[idx])
fill_between_steps(ax, times, subset, color=colors[idx], zorder=30-(idx*2), **kwargs)
#last
idx += 1
try:
inds = usearr<=levels[idx-1]
subset = np.asarray(dmcopy(usearr))
subset[inds] = np.nan
kwargs['label'] = '>{0}'.format(levels[-1])
fill_between_steps(ax, times, subset, color=colors[idx], zorder=30-(idx*2), **kwargs)
except:
pass
#if required, set x axis to times
if tflag:
try:
applySmartTimeTicks(ax, data[time])
except (IndexError, KeyError):
#using data array to index, so should just use time
applySmartTimeTicks(ax, time)
ax.grid('off', which='minor') #minor grid usually looks bad on these...
if legend:
ncols = len(levels)+1
if ncols > 3: ncols = ncols//2
ax.legend(loc='upper left', ncol=ncols)
return ax
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
dum = mpl.rc_params_from_file(usestyle)
styapply = dict()
#remove None values as these seem to cause issues...
for key in dum:
if dum[key] is not None: styapply[key] = dum[key]
for key in styapply:
mpl.rcParams[key] = styapply[key]
mpl.rcParams['image.cmap'] = cmap
#save current rcParams before applying spacepy style
oldParams = dict()
for key, val in mpl.rcParams.items():
oldParams[key] = dmcopy(val)
if config['apply_plot_styles']:
style()
def revert_style():
'''Revert plot style settings to those in use prior to importing spacepy.plot
'''
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for key in oldParams:
try:
mpl.rcParams[key] = oldParams[key]
except ValueError:
pass
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
