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