''' Resamples a spectrogram from an irregular sampling in linear frequency space

to a regular grid in log frequency space.

'''

from scipy import interpolate

import numpy as np

nf = len(fghz)

fghzl = np.logspace(np.log10(fghz[0]),np.log10(fghz[-1]),nf)

x, y = np.meshgrid(ut,fghzl)

fint = interpolate.interp2d(ut,fghz,tsys,kind='cubic')

out = fint(ut,fghzl)

''' Resamples a spectrogram from an irregular sampling in linear frequency space

to a regular grid in linear frequency space.

'''

from scipy import interpolate

import numpy as np

nf = len(fghz)

fghzl = np.linspace(fghz[0],fghz[-1],nf)

x, y = np.meshgrid(ut,fghzl)

fint = interpolate.interp2d(ut,fghz,tsys)#,kind='cubic')

out = fint(ut,fghzl)

''' Creates standard spectrogram plot for EOVSA data, using axes supplied

or creates a new single axis plot if None. The intensities are log-scaled,

the xaxis is interpreted as time (ut can be timestamps or plot_date format)

kwargs:

dmin Clip data to this minimum value [sfu] (default = 10 sfu)

dmax Clip data to this maximum value [sfu] (default = tsys.max())

xlabel String to use as xlabel

(default is 'Time [UT on YYYY-MM-DD]' if ax is supplied--none otherwise)

ylabel String to use as ylabel

(default is 'Frequency [GHz]' if ax is supplied--none otherwise)

title String to use as plot title

(default is no title)

logsample Boolean. If True, resample the data on a logarithmic

frequency space, if False resample on a linear space,

if None, do no resampling in frequency

xdata Boolean. If True, label graph for cross-correlation. If False,

or omitted, lable graph for Total Power

'''

import matplotlib.pylab as plt

import matplotlib.dates

import numpy as np

utd = ut.plot_date

datstr = ut[0].iso[:10]

if ax is None:

# No axes supplied, so create one (and assume labels are wanted)

f, ax = plt.subplots(1,1)

ax.set_xlabel('Time [UT on '+datstr+']')

ax.set_ylabel('Frequency [GHz]')

ax.set_title('EOVSA Total Power for '+datstr)

if 'xdata' in kwargs.keys():

if kwargs['xdata'] is True:

ax.set_title('EOVSA Summed Cross-Correlation Amplitude for '+datstr)

ax.xaxis.set_tick_params(width=1.5,size=10,which='both')

ax.yaxis.set_tick_params(width=1.5,size=10,which='both')

if logsample:

# Sample data only a uniform logarithmic frequency space

fghzl, tsysl = log_sample(fghz, utd, tsys)

ax.set_yscale('log')

minorFormatter = plt.LogFormatter(base=10, labelOnlyBase=False)

ax.yaxis.set_minor_formatter(minorFormatter)

elif logsample is None:

# No sampling in frequency space

fghzl, tsysl = fghz, tsys

else:

# logsample is not True or None (presumably False), so resample

# data on a uniform linear frequency space

fghzl, tsysl = lin_sample(fghz, utd, tsys)

dmin = 1.

if 'dmin' in kwargs.keys():

if kwargs['dmin'] is not None:

dmin = kwargs['dmin']

dmax = tsys.max()

if 'dmax' in kwargs.keys():

if kwargs['dmax'] is not None:

dmax = kwargs['dmax']

# Take logarithm if TP, but not for Cross-correlation amplitude

data = np.log10(np.clip(tsysl,dmin,dmax))

if 'xdata' in kwargs.keys():

if kwargs['xdata'] is True:

data = np.clip(tsysl,dmin,dmax)

im = ax.imshow(data,origin='lower',extent=[utd[0],utd[-1],fghzl[0],fghzl[-1]],

aspect='auto',interpolation='nearest')

if cbar:

cbar_label = 'Log Flux Density [sfu]'

if 'xdata' in kwargs.keys():

if kwargs['xdata'] is True:

cbar_label = 'Amplitude [arb. units]'

plt.colorbar(im,ax=ax,label=cbar_label)

ax.xaxis_date()

ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M:%S"))

# Set labels if requested

if 'xlabel' in kwargs.keys():

if kwargs['xlabel'] == 'auto':

ax.set_xlabel('Time [UT on '+datstr+']')

else:

ax.set_xlabel(kwargs['xlabel'])

if 'ylabel' in kwargs.keys():

if kwargs['ylabel'] == 'auto':

ax.set_ylabel('Frequency [GHz]')

else:

ax.set_ylabel(kwargs['ylabel'])

if 'title' in kwargs.keys():

ax.set_title(kwargs['title'])

def __init__(self,trange):

''' Create the object for the specified timerange specified by the

2-element Time() trange. The timerange is used to create a list

of Miriad database files to read, and the data are read.

'''

# Read data, assuming 16-element correlator if after 2016 May 1

if trange[0].lv > 3544905600.0:

out = dump_tsys.rd_miriad_tsys_16(trange)

else:

out = dump_tsys.rd_miriad_tsys(trange)

nant, npol, nf, nt = out['tsys'].shape

self.xdata = out['tsys'][:,0,:,:]

self.ydata = out['tsys'][:,1,:,:]

self.fghz = out['fghz']

self.time = Time(out['ut_mjd'],format='mjd')

self.tidx = [0,len(self.time)]

# Read calibration

fghz, self.calfac, self.offsun = offline.read_dbcalfac(trange[0])

# Make sure frequencies in data and calibration agree

calidx, dataidx = common_val_idx((fghz*1000).astype('int'),(self.fghz*1000).astype('int'))

self.bidx = [0,100]

self.fghz = self.fghz[dataidx]

self.xdata = self.xdata[:,dataidx,:]

self.ydata = self.ydata[:,dataidx,:]

if self.calfac is not None:

# Select frequencies and swap axes to put into standard form

self.calfac = np.rollaxis(self.calfac[:,calidx,:],2)

self.offsun = np.rollaxis(self.offsun[:,calidx,:],2)

fghz = fghz[calidx]

# Set frequency index range (fidx) to default to show only frequencies > 2.5 GHz

lowf, = np.where(self.fghz > 2.5)

self.fidx = [lowf[0],len(self.fghz)]

self.drange = [None,None]

self.antlist = range(nant)

self.cbar = True

self.showants = range(nant)

self.domedian = True

self.docal = True

self.dolog = False

self.dosub = True

self.ax = None

self.version = __version__

def show(self):

''' Create a spectrogram plot of the data

'''

tsys, stdtsys = self.get_data()

if self.domedian:

# This results in only a single plot

self.ax = plot_spectrogram(self.fghz[self.fidx[0]:self.fidx[1]], self.time[self.tidx[0]:self.tidx[1]], tsys, ax=self.ax, cbar=self.cbar, logsample=self.dolog, dmin=self.drange[0], dmax=self.drange[1])

else:

print 'Cannot (yet) plot data for each anteanna separately. Please set <self>.domedian = True first'

def get_median_data(self, xtsys=None, ytsys=None):

''' Get optionally calibrated, optionally background subtracted

data as median over polarization and antenna list in self.showants

'''

if xtsys is None:

if self.docal:

# Do calibration and optionally subtraction

xtsys, ytsys = self.get_cal_data()

if self.dosub:

xtsys, ytsys = self.get_bgsub_data(xtsys, ytsys)

elif self.dosub:

# No calibration, so select data and do subtraction

xtsys = self.xdata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

ytsys = self.ydata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

xtsys, ytsys = self.get_bgsub_data(xtsys, ytsys)

medxtsys = np.nanmedian(xtsys[self.showants,:,:],0)

stdxtsys = np.nanstd(xtsys[self.showants,:,:],0)

medytsys = np.nanmedian(ytsys[self.showants,:,:],0)

stdytsys = np.nanstd(ytsys[self.showants,:,:],0)

tsys = (medxtsys+medytsys)/2.

stdtsys = np.sqrt(stdxtsys**2 + stdytsys**2)/2.

return tsys, stdtsys

def get_cal_data(self):

# Select data

xtsys = np.zeros((len(self.antlist), self.fidx[1] - self.fidx[0], self.tidx[1] - self.tidx[0]),dtype='float')

ytsys = np.zeros((len(self.antlist), self.fidx[1] - self.fidx[0], self.tidx[1] - self.tidx[0]),dtype='float')

# Apply calibration

for i,j in enumerate(range(self.tidx[0], self.tidx[1])):

xtsys[:,:,i] = ((self.xdata[self.antlist, self.fidx[0]:self.fidx[1], j] -

self.offsun[self.antlist, 0, self.fidx[0]:self.fidx[1]])

*self.calfac[self.antlist, 0, self.fidx[0]:self.fidx[1]])

ytsys[:,:,i] = ((self.ydata[self.antlist, self.fidx[0]:self.fidx[1], j] -

self.offsun[self.antlist, 1, self.fidx[0]:self.fidx[1]])

*self.calfac[self.antlist, 1, self.fidx[0]:self.fidx[1]])

return xtsys, ytsys

def get_bgsub_data(self,xtsys=None, ytsys=None):

''' Get optionally calibrated data after background subtraction is applied.

'''

if xtsys is None:

if self.docal:

# Do calibration and optionally subtraction

xtsys, ytsys = self.get_cal_data()

if self.dosub:

xtsys, ytsys = self.get_bgsub_data(xtsys, ytsys)

else:

# No calibration, so select data and raw subtraction

xtsys = self.xdata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

ytsys = self.ydata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

# Perform the background subtraction

bgx, bgy = self.getbg(self.bidx, xtsys, ytsys)

nt = self.tidx[1] - self.tidx[0]

nf = self.fidx[1] - self.fidx[0]

for i in range(nt):

xtsys[:,0:nf,i] -= bgx

ytsys[:,0:nf,i] -= bgy

return xtsys, ytsys

def get_data(self):

''' Get optionally calibrated, optionally background-subtracted data.

If self.domedian is True, return the median of data over polarization

and antenna list in self.showants.

'''

if self.docal:

xtsys, ytsys = self.get_cal_data()

else:

xtsys = self.xdata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

ytsys = self.ydata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

if self.dosub:

xtsys, ytsys = self.get_bgsub_data(xtsys, ytsys)

if self.domedian:

tsys, stdtsys = self.get_median_data(xtsys, ytsys)

else:

tsys = np.swapaxes(np.array([xtsys, ytsys]),1,0)

stdtsys = None

return tsys, stdtsys

def getbg(self, bidx=None, xtsys=None, ytsys=None):

''' Get background spectra for each antenna and polarization, applying

calibration first if indicated by self.docal = True

'''

if bidx is None:

bidx = self.bidx

else:

self.bidx = bidx

if xtsys is None:

# No data supplied, so generate it

if self.docal:

# Do calibration

xtsys, ytsys = self.get_cal_data()

else:

# No calibration desired, so just select raw data

xtsys = self.xdata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

ytsys = self.ydata[self.antlist,self.fidx[0]:self.fidx[1],self.tidx[0]:self.tidx[1]]

# Generate median over supplied background indexes. These are spectra for each antenna in self.antlist

bgx = np.nanmedian(xtsys[:,:,bidx[0]:bidx[1]],2)

bgy = np.nanmedian(ytsys[:,:,bidx[0]:bidx[1]],2)

return bgx, bgy

def explore(self):

''' Like show(), but provides a mouse-driven interface for exploring the plot

after creation. Only works for median data.

'''

import matplotlib.pylab as plt

tsys, stdtsys = self.get_median_data()

dlogtsys = stdtsys/tsys

fig = plt.figure(figsize=(8,6))

fig.suptitle('EOVSA Spectrogram '+self.time[self.tidx[0]].iso[:10],fontsize=25)

spectrogram_ax = plt.axes([0.1,0.4,0.6,0.5])

spectrogram_ax.set_ylabel('Frequency [GHz]')

spectrum_ax = plt.axes([0.75,0.4,0.23,0.5])

spectrum_ax.set_ylim(1.,tsys.max())

try:

spectrum_ax.set_yscale('log')

except:

pass

spectrum_ax.set_xscale('log')

spectrum_ax.set_xlim(1,18)

spectrum_ax.set_ylabel('Flux Density [sfu]')

spectrum_ax.set_xlabel('Frequency [GHz]')

# Set initial spectruma and lightcurve to correspond to mid-range

# time and frequency

fghz = self.fghz[self.fidx[0]:self.fidx[1]]

t = self.time[self.tidx[0]:self.tidx[1]].plot_date

midf = len(fghz)/2

midt = len(t)/2

spec, = spectrum_ax.plot(fghz,tsys[:,midt],'.')

p, ffit, sfit = tpfit(np.log(fghz),np.log(tsys[:,midt]),sigma=dlogtsys[:,midt])

specfit, = spectrum_ax.plot(np.exp(ffit),np.exp(sfit))

specpt, = spectrum_ax.plot(fghz[midf],tsys[midf,midt],'<',markersize=5,c='y')

if self.cbar:

lc_ax = plt.axes([0.1,0.1,0.48,0.25],sharex=spectrogram_ax)

else:

lc_ax = plt.axes([0.1,0.1,0.6,0.25],sharex=spectrogram_ax)

lc, = lc_ax.plot_date(t,tsys[midf,:],'-')

lcpt, = lc_ax.plot_date(t[midt],tsys[midf,midt],'^',markersize=5,c='y')

lc_ax.set_ylim(1.,tsys.max())

try:

lc_ax.set_yscale('log')

except:

pass

lc_ax.set_ylabel('Flux Density [sfu]')

lc_ax.set_xlabel('Time [UT]')

tstr = Time(t[midt],format='plot_date').iso[11:19]

lctxt = lc_ax.text(0.02,0.9,'{:} UT, {:0.3f} GHz, {:0.3f} sfu'.format(tstr,fghz[midf],tsys[midf,midt]), transform=lc_ax.transAxes)

sptxt = spectrum_ax.text(0.02,0.9,'{:} UT, {:0.3f} GHz, {:0.3f} sfu'.format(tstr,fghz[midf],tsys[midf,midt]), transform=spectrum_ax.transAxes)

self.ax = spectrogram_ax

self.show()

def find_ij(x, y):

return abs(utd - x).argmin(), abs(fghz - y).argmin()

def onmove(event):

if event.inaxes != spectrogram_ax: return

i, j = abs(t - event.xdata).argmin(), abs(fghz - event.ydata).argmin()

#print 'indexes are t=%f, f=%f'%(i, j)

spec.set_data(fghz,tsys[:,i])

p, ffit, sfit = tpfit(np.log(fghz),np.log(tsys[:,i]),sigma=dlogtsys[:,i])

specfit.set_data(np.exp(ffit),np.exp(sfit))

specpt.set_data(fghz[j],tsys[j,i])

lc.set_data(t,tsys[j,:])

lcpt.set_data(t[i],tsys[j,i])

tstr = Time(t[i],format='plot_date').iso[11:19]

lctxt.set_text('{:} UT, {:0.3f} GHz, {:0.3f} sfu'.format(tstr,fghz[j],tsys[j,i]))

sptxt.set_text('{:} UT, {:0.3f} GHz, {:0.3f} sfu'.format(tstr,fghz[j],tsys[j,i]))

fig.canvas.draw()

def onclick(event):

if event.inaxes != spectrogram_ax: return

# print 'Turning on mouse-move events'

fig.mid = fig.canvas.mpl_connect('motion_notify_event', onmove)

def onrelease(event):

if event.inaxes != spectrogram_ax: return

# print 'Turning off mouse-move events'

fig.canvas.mpl_disconnect(fig.mid)

self.cid = fig.canvas.mpl_connect('button_press_event', onclick)

self.rid = fig.canvas.mpl_connect('button_release_event', onrelease)

def get_fit(self):

''' Returns an array (in this order) of peak frequency, peak flux density

low-frequency index (slope), and high-frequency index (slope) for each

time in self.tidx. This is only valid for calibrated, background-

subtracted burst emission.

'''

tsys, stdtsys = self.get_median_data()

nf, nt = tsys.shape

self.pfit = np.zeros((4,nt))

dlogtsys = stdtsys/tsys

for i in range(nt):

p, ffit, sfit = tpfit(np.log(self.fghz[self.fidx[0]:self.fidx[1]]),np.log(tsys[:,i]),sigma=dlogtsys[:,i])

# Calculate and return physical parameters S_pk, f_pk, alpha and beta

# Note that if the fit does not work, S_pk and f_pk are determined from the

# data, not the fit, and the slopes are nan.

S_pk = np.exp(sfit).max()

f_pk = np.exp(ffit[sfit.argmax()])

lf_slope = p[1]

hf_slope = p[1]-p[3]

''' Function called by curve_fit() to evaluate fitting function.

'''

# Original Staehli expression S = exp(A)*f^alpha*[1-exp(-exp(B)*f^(-beta))]

# can be written log S = A + alpha*log(f) + log(1-exp(-exp(B-beta*log(f))))

# Hence, in the expression below:

# x = log(f) [base-e]

# a = A

# b = alpha

# c = B

# d = beta

# Physical parameters are:

# Low-frequency slope = b

# High-frequency slope = alpha - beta = b - d

# Analytical expressions for S_pk and f_pk are not available, so

# the peak frequency and flux density are determined from the

# fit array

''' Given a set of log(frequencies) X and total power log(flux density) at each

frequency, fit the data with the Staehli function and return the fit parameters and

x,y arrays of smooth, fitted data '''

from scipy.optimize import curve_fit

# This complains if there are nans or infs in the data, so remove them first

X = X[np.isfinite(data)]

data = data[np.isfinite(data)]

if sigma is not None:

sigma = sigma[np.isfinite(data)]

if len(data) < 4:

return np.zeros(4),np.array([0,1]),np.array([0,1])

y = data

x = np.linspace(0,2.9,20000)

try:

params, pcov = curve_fit(peval, X, y, sigma=sigma)

a, b, c, d = params

y = peval(x, a, b, c, d)

except:

params = [np.nan]*4