def plot_a(ax,
age,
flux,
low=None,
high=None,
syms='o',
colors='b',
sizes=4,
alphas=1.0,
mecs='black',
mews=0.5,
line='',
errorband=False,
label='_nolegend_'):
if low is not None:
elow, ehigh = ebars(flux, low, high)
else:
elow = None
ehigh = None
fancy_plot(age,
flux,
yerror=elow,
yerror_high=ehigh,
syms=syms,
colors=colors,
sizes=sizes,
alphas=alphas,
mecs=mecs,
errorband=errorband,
line=line,
mews=mews)
def plot_a(ax,age,flux,low=None,high=None,syms='o',colors='b',sizes=4,alphas=1.0,mecs='black',mews=0.5,line='',errorband=False,label='_nolegend_'):
if low is not None:
elow,ehigh = ebars(flux,low,high)
else:
elow = None
ehigh = None
fancy_plot(age,flux,yerror=elow,yerror_high=ehigh,syms=syms,colors=colors,sizes=sizes,alphas=alphas,mecs=mecs,errorband=errorband,line=line,mews=mews)
def make_plot(self):
#plot solar wind speed
self.ax[0].scatter(self.soho_df.index,
self.soho_df.SPEED,
color='black')
self.ax[0].set_ylabel('$|\mathrm{V}|$ [km/s]')
fancy_plot(self.ax[0])
#plot solar wind density
self.ax[1].scatter(self.soho_df.index, self.soho_df.Np, color='black')
self.ax[1].set_ylabel('n$_\mathrm{p}$ [cm$^{-3}$]')
self.ax[1].set_yscale('log')
fancy_plot(self.ax[1])
#Thermal Speed
self.ax[2].scatter(self.soho_df.index, self.soho_df.Vth, color='black')
self.ax[2].set_ylabel('w$_\mathrm{p}$ [km/s]')
self.ax[2].set_xlabel('Time')
fancy_plot(self.ax[2])
#set plotting limit
self.set_day_limit()
#--Initialize Plot--
ax1 = start_plot(xtitle='SN1987A Age [days]',
ytitle='Radius [arcsec]',
xmin=args.agemin,
xmax=args.agemax,
ymin=rmin,
ymax=rmax)
#----Plot----
fancy_plot(fitdata['age'],
fitdata['R0'],
yerror=fitdata['R0errl'],
yerror_high=fitdata['R0erru'],
syms=syms,
colors=colors,
sizes=sizes,
alphas=alphas,
mecs=mecs,
mews=mews,
errorband=False)
#----Plot Legend----
plot_legend(labels=leglabels,
colors=legcolors,
markers=legsyms,
mecs=legmecs,
mews=legmews,
fontsize='small')
#--set top axis--
#get predictions for the Mission long CELIAS mission
if full_soho:
full_df['predict'] = sh_rs.predict(full_df[use_cols])
best_df = full_df[full_df.predict >= 0.90]
best_df.to_csv('../soho/archive_shocks.csv', sep=';')
#create figure object
fig, ax = plt.subplots(figsize=(12, 7))
#plot solar wind speed
ax.scatter(sig_jump, sig_cnts, color='black')
ax.set_xlabel(r'N sigma training')
ax.set_ylabel(r'\# of Events (2016)')
ax.set_yscale('log')
ax.set_ylim([.5, 2E6])
fancy_plot(ax)
fig.savefig('../plots/soho_num_events_sig_cut.png',
bbox_inches='tight',
bbox_pad=0.1)
fig.savefig('../plots/soho_num_events_sig_cut.eps',
bbox_inches='tight',
bbox_pad=0.1)
###########
#BOKEH PLOT
#For the training set
###########
from bokeh.models import HoverTool, ColumnDataSource
from bokeh.plotting import figure, show, save
from bokeh.layouts import column, gridplot
def main(self):
ptype = self.ptype
#format name of file to read
fname = 'offset30{0}.dat'.format(ptype[0].lower())
#read in trend values for given ccd
dat = readsav(fname)
#correct for different variable name formats
aval = ''
if self.ptype.lower() == 'nuv': aval = 'n'
#put readsav arrays in defining variables
time = dat['t{0}i'.format(aval)] #seconds since Jan. 1st 1979
port = dat['av{0}i'.format(aval)]
errs = dat['sigmx']
#cut spurious point from fit
if self.cut:
good, = np.where((time <= 1.175e+09) | (time >= 1.180e+09))
time = time[good]
port = port[good, :]
errs = errs[good, :]
print ' {0:10},{3:10},{2:15},{1:10},{4:10},{5:20},{6:20},{7:10}'.format(
'Amp1', 'Phi1', 'P1', 'Amp2', 'Phi2', 'Trend', 'Quad', 'Offset')
#loop over all ports
for i in range(port.shape[1]):
toff = self.t0dict['{0}{1:1d}'.format(ptype.lower(), i + 1)]
dt0 = time - toff #- 31556926.#makes times identical to the values taken in iris_trend_fix
# init parameter guesses
guess = self.gdict['{0}{1:1d}'.format(ptype.lower(), i + 1)]
#fit with initial guesses
if self.fit:
popt, pcov = curve_fit(self.offset,
dt0,
port[:, i],
p0=guess,
sigma=errs[:, i])
else:
popt = guess
#print '{0} {1:1d}'.format(self.ptype.upper(),i+1)
if self.fit:
print '{8}{9:1d}=[{0:^10.5f},{3:^10.5f},{2:^15.4e},{1:^10.5f},{4:^10.5f},{5:^20.9e},{6:^20.9e},{7:^10.5f}]'.format(
popt[0], popt[1], popt[2], popt[3], popt[4], popt[5],
popt[6], popt[7], self.ptype, i + 1)
fig, ax = plt.subplots()
ptim = np.linspace(dt0.min(), dt0.max() + 1e3, 500)
#print dt0.max()
#print self.offset(dt0.max(),*popt)
#print self.offset(1.2041407e8,*popt)
ptime = [self.startd + timedelta(seconds=j + toff) for j in ptim]
dt1 = [self.startd + timedelta(seconds=j) for j in time]
ax.set_title('{0} {1:1d}'.format(self.ptype.upper(), i + 1))
ax.plot(ptim, self.offset(ptim, *popt), 'r--', label='fit')
ax.scatter(dt0, port[:, i], color='black')
ax.errorbar(dt0,
port[:, i],
yerr=errs[:, i],
color='black',
fmt='o')
if self.fit:
var = np.sqrt(
np.sum((port[:, i] - self.offset(dt0, *popt))**2.) /
float(dt0.size))
else:
var = np.sqrt(
np.sum((port[:, i] - self.offset(dt0, *guess))**2.) /
float(dt0.size))
ax.text(dt0.min(), 8, r'$\sigma$(fit) = {0:6.5f}'.format(var))
ax.set_ylim([-5., 15.])
ax.set_xlabel('Epoch Time [s]', fontsize=18)
ax.set_ylabel('Counts [ADU]', fontsize=18)
fancy_plot(ax)
# plt.show()
fig.savefig('plots/new_fits/{0}_port{1:1d}.png'.format(
self.ptype, i + 1),
bbox_pad=.1,
bbox_inches='tight')
if self.show: plt.show()
def iris_dark_plot(self):
"""
This function will plot and IRIS pedestal data and the best fit parameters for each port.
Args
------
self: class
The GUI class in this module
Returns
--------
None
"""
#clear the plot axes
for i in self.wplot.keys():
#If a previous plot exists get x and y limits
if self.lat_plot:
#get previous x and y limits
xlim = self.wplot[i].get_xlim()
ylim = self.wplot[i].get_ylim()
#clear previous plot
self.wplot[i].clear()
self.wplot[i].set_title(i.upper())
self.wplot[i].set_xlabel('Offset Time [s]')
self.wplot[i].set_ylabel('Pedestal Offset [ADU]')
#If a previous plot exists set x and y limits
if self.lat_plot:
#set previous x and y limits
self.wplot[i].set_xlim(xlim)
self.wplot[i].set_ylim(ylim)
#After first run through set lat(er)_plots to true
self.lat_plot = True
#best fit lines
self.bline = {}
#data scatter
self.sdata = {}
#plot data for all IRIS dark remaining pedestals
for i in sorted(self.fdata.keys()):
#Get plot associated with each port
ax = self.wplot[i[:-1]]
#Put data in temp array
dat = self.fdata[i]
#set ptype attribute for curvefit and offset model
self.ptype = i[:-1]
#set the current port variable
self.cport = i
#get variance in best fit model
var = self.get_var(i, self.gdict[i])
#get offset in last data point
last = self.get_last(i, self.gdict[i])
#Uploaded best fit
#current dark time plus a few hours
ptim = np.linspace(self.fdata[i][0].min(),
self.fdata[i][0].max() + 1e3, 500)
#plot values currently in gdict for best fit values (store in dictionary for updating line)
self.bline[i] = self.wplot[i[:-1]].plot(
ptim,
self.offset(ptim, *self.gdict[i]),
color=self.fdata[i][3],
label='{0}(Unc.) = {1:5.4f}'.format(i, var))
#plot each port
self.sdata[i] = ax.scatter(dat[0],
dat[1],
color=dat[3],
marker=dat[4],
label='{0}(last) = {1:3.2f}'.format(
i, last))
ax.errorbar(dat[0],
dat[1],
yerr=dat[2],
color=dat[3],
fmt=dat[4],
label=None)
#add legend
for i in self.wplot.keys():
self.wplot[i].legend(loc='upper left', frameon=True)
#add fancy plotting
fancy_plot(self.wplot[i])
self.canvas.draw()
alphas = [1.0]*nobs
print 'Markers Defined'
#----Set Up Plot File----
#pdffile = PdfPages(args.plotfile)
with PdfPages(args.plotfile) as pdffile:
#----R0 vs age----
#--Initialize Plot--
ax1 = start_plot(xtitle='SN1987A Age [days]',ytitle='Radius [arcsec]',xmin=args.agemin,xmax=args.agemax,ymin=rmin,ymax=rmax)
#----Plot----
fancy_plot(ax1,fitdata['age'],fitdata['R0'],yerror=fitdata['R0errl'],yerror_high=fitdata['R0erru'],syms=syms,colors=colors,sizes=sizes,alphas=alphas,mecs=mecs,mews=mews,errorband=False)
#----Plot broken-linear fits----
if args.plotfit == 'yes':
for bi in range(len(bands)):
b = bands[bi]
#--read best-fit model file--
modeldata = np.genfromtxt(radiusfilename+'_'+b+'_mpfit_radii.txt',skip_header=0,names=True,comments='#',dtype=None)
#--overplot model--
fancy_plot(ax1,modeldata['age'],modeldata['radius'],yerror=modeldata['radiuslow'],yerror_high=modeldata['radiushigh'],syms='None',colors=bandcolors[bi],line='-',errorband=True)
#----Other noteable data points----
# [age,radius(arcsec from center)]
if args.plotextras != None:
def chi_min(p_mat,
par,
rgh_chi_t,
plsm,
k,
ref_chi_t=pd.to_timedelta('10 minutes'),
refine=True,
n_fine=4,
plot=False):
"""
chi_min computes the chi^2 min. time for a given set of parameters
p_mat: Pandas DataFrame
Solar wind pandas DataFrame for a space craft roughly sampled
par : list
List of parameters to use in the Chi^2 minimization
rgh_chi_t: Pandas datetime delta object
Time around a given time in p_mat to include in chi^2 minimization
ref_chi_t: Pandas datetime delta object
Time around a given time in refined grid to include in chi^2 minimization
plsm: dict
Dictionary of plasma DataFrames of plasma Dataframes
k : string
Spacecraft name in plsm dictionary
refine: boolean
Whether to use a refined window (Default = True)
n_fine : integer
Number of chi^2 min values from the rough grid to check with the fine grid (Default = 4)
plot : boolean
Plot Chi^2 minimization window (Default = False)
RETURNS
--------
i_min : datetime index
Datetime index of best fit Chi^2 time
"""
#set up chisquared array in pandas object
p_mat.loc[:, 'chisq'] = -99999.9
for time in p_mat.index:
#get a region around one of the best fit times
com_slice = [time - rgh_chi_t, time + rgh_chi_t]
c_mat = plsm[k].loc[com_slice[0]:com_slice[1]]
#update the time index of the match array for comparision with training spacecraft (i=training spacecraft time)
c_mat.index = c_mat.index + (i - time)
#remove Speed fill values by interpolation
c_mat.loc[c_mat.SPEED < 0., 'SPEED'] = np.nan
c_mat.SPEED.interpolate('time', inplace=True)
#get training spacecraft time range
t_mat = plsm[trainer].loc[c_mat.index.min():c_mat.index.max()]
#resample the matching (nontrained spacecraft to the trained spacecraft's timegrid and interpolate
c_mat = c_mat.reindex(t_mat.index,
method='nearest').interpolate('time')
#get median offset to apply to match spacecraft
off_speed = c_mat.SPEED.median() - t_mat.SPEED.median()
c_mat.SPEED = c_mat.SPEED - off_speed
#compute chi^2 value for Wind and other spacecraft
p_mat.loc[time, 'chisq'] = np.sqrt(
np.sum(((c_mat.loc[:, par] - t_mat.loc[:, par])**2.).values))
#create figure to check matchin
if plot:
ax.scatter(
c_mat.index,
c_mat.SPEED,
label=time.to_pydatetime().strftime('%Y/%m/%dT%H:%M:%S') +
' chisq = {0:4.0f}'.format(p_mat.loc[time, 'chisq']))
ax.plot(t_mat.index, t_mat.SPEED, label='', color='black')
if plot:
ax.legend(loc='upper left', frameon=False, scatterpoints=1)
ax.set_xlim([t_mat.index.min(), t_mat.index.max()])
ax.set_ylabel('Speed [km/s]')
ax.set_xlabel('Time [UTC]')
fancy_plot(ax)
#ax.set_ylim([300,1000.])
plt.show()
#get the index of minimum chisq value
i_min = p_mat['chisq'].idxmin()
#use fine grid around observations to match time offset locally
if refine:
#Find 4 lowest chisq min times
p_temp = p_mat.sort_values('chisq', ascending=True)[:n_fine]
#get all values in top n_fine at full resolution
p_mat = plsm[k].loc[p_temp.index.min() - rgh_chi_t:p_temp.index.max() +
rgh_chi_t]
print(p_temp.index.min() - rgh_chi_t, p_temp.index.max() + rgh_chi_t)
#loop over all indexes in refined time window
for time in p_mat.index:
#get a region around one of the best fit times
com_slice = [time - ref_chi_t, time + ref_chi_t]
c_mat = plsm[k].loc[com_slice[0]:com_slice[1]]
#update the time index of the match array for comparision with training spacecraft (i=training spacecraft time)
c_mat.index = c_mat.index + (i - time)
#remove Speed fill values by interpolation
c_mat.loc[c_mat.SPEED < 0., 'SPEED'] = np.nan
c_mat.SPEED.interpolate('time', inplace=True)
#get trainint spacecraft time range
t_mat = plsm[trainer].loc[c_mat.index.min():c_mat.index.max()]
#resample the matching (nontrained spacecraft to the trained spacecraft's timegrid and interpolate
c_mat = c_mat.reindex(t_mat.index,
method='nearest').interpolate('time')
#get median offset to apply to match spacecraft
off_speed = c_mat.SPEED.median() - t_mat.SPEED.median()
c_mat.SPEED = c_mat.SPEED - off_speed
#compute the chisq value in SPEED from the top ten probablilty array including the median offsets
p_mat.loc[time, 'chisq'] = np.sqrt(
np.sum(((c_mat.loc[:, par] - t_mat.loc[:, par])**2.).values))
#get the index of minimum refined chisq value
i_min = p_mat['chisq'].idxmin()
return i_min
def plot_extras(names='all', agemin=4000, agemax=11000):
"""
Author: Kari A. Frank
Date: September 22, 2015
Purpose: Plot lines on already open age vs Y plots (where Y is
typically either radius or flux
Input:
names : string
comma separated list of names of which extras to plot.
default = 'all'. options are:
'plait1995'- plots position and width of the ER from
Plait1995 (in this case Y must be radius)
'ng2013' - plots the radio break age day=7600 from the
Ng2013 torus fits to radio images, as vertical line
'sugarman2002' - plots location of the ring from Sugarman 2002
(in this case Y must be radius)
'hotspots' - plots the radius and day of appearance for the
HST hot spots in Sugarman 2002
'larsson2014' - plots the age of transition from radioactive
decay to X-ray heating as inner debris energy source
'chandrabroadbreak' - plot as vertical line the best fit
age of the break point in expansion curve
'plaitwidth' - plot distance from chandrabroadbreak radius
to width of plait ring
Output:
- plots to an already open plot
Usage Notes:
- will not add anything to the plot legend
"""
dummy_y = [0.00000001, 1000.] #dummy y values for vertical lines
dummy_sym = '.'
if names == 'all' or 'hotspots' in names:
# hot spot appearances in HST, Sugarman2002 table 1
hotspot_ages = [
2933, 4283, 4337, 4337, 4440, 4440, 4440, 4725, 4816, 4816, 4999,
4999
]
hotspot_radii = [
0.56, 0.673, 0.607, 0.702, 0.565, 0.697, 0.707, 0.607, 0.535,
0.629, 0.531, 0.713
]
fancy_plot(hotspot_ages, hotspot_radii, syms='*', colors='white')
# legcolors+=['white'] #marker will look like border only
# leglabels+=['HST Hot Spots']
# legmecs+=['black']
# legmews+=[0.5]
# legsyms+=['*']
if names == 'all' or 'larsson2014' in names:
#Larsson2013 -- inner debris (HST) morphology transitioned
# from core to edge-brightened due to
# energy source shifting from radioactive decay to X-ray
# illumination
debris_transition_age = [5500, 5500]
debris_transition_radius = dummy_y #plot vertical line
fancy_plot(debris_transition_age,
debris_transition_radius,
colors='black',
line='--',
syms=dummy_sym)
#legcolors+=[None] #marker will look like border only
#leglabels+=['Debris Transition']
#legmecs+=[None]
#legmews+=[0.]
#legsyms+=[None]
if names == 'all' or 'plait1995' in names:
# Width and location of optical ring from UV flash,
# Plait1995
# plot horizontal band
plaitwidth = np.array([0.121 / 2.0, 0.121 / 2.0])
plait_radii = [0.86, 0.86]
plait_ages = [agemin, agemax]
fancy_plot(plait_ages,
plait_radii,
colors='gray',
syms=dummy_sym,
errorband=True,
yerror=plaitwidth)
if names == 'all' or 'plaitwidth' in names:
# width of Plait ring, from chandra break point
plaitwidth = np.array([0.121 / 2.0, 0.121 / 2.0])
plait_ages = [-1.0, 20000.0]
chandrabreakradii = [0.73 + 0.121 / 2.0, 0.73 + 0.121 / 2.0]
fancy_plot(plait_ages,
chandrabreakradii,
colors='purple',
syms=dummy_sym,
errorband=True,
yerror=plaitwidth)
if names == 'all' or 'sugarman2002' in names:
# Location of ring from Sugarman2002 (table 3)
sugarman_radii = [0.829, 0.829]
sugarman_ages = [agemin, agemax]
fancy_plot(plait_ages,
plait_radii,
colors='gray',
syms=dummy_sym,
errorband=True,
yerror=plaitwidth)
if names == 'all' or 'ng2013' in names:
#Ng2013 Radio expansion measurements: using the torus
# model found the following all around day 7600:
# - break in expansion (decrease in velocity)
# - break in torus opening angle, from constant ~40deg
# to decreasing
# - break in asymmetry; the morphology was ~40% asymmetric,
# but became steadily more symmetric at the break
# - break in the light curve; the slope flattened,
# deviating from previous exponential growth
radiobreak_radii = dummy_y #plot vertical line
radiobreak_age = [7600, 7600]
fancy_plot(radiobreak_age,
radiobreak_radii,
syms=dummy_sym,
colors='black',
line=':')
if names == 'all' or 'chandrabroadbreak' in names:
# plot best fit age of the break in expansion from the
# 300-8000 chandra images
chandrabreak_radii = dummy_y #plot vertical line
chandrabreak_age = [5962.77, 5962.77]
fancy_plot(chandrabreak_age,
chandrabreak_radii,
syms=dummy_sym,
colors='black',
line='--')
if (names == 'all' or 'ng2013radii' in names or 'ng2013fluxes' in names):
ngradiifile = ('/astro/research/kaf33/Dropbox/Research/SN1987A'
'/ng2013_ring_radii.txt')
ng2013_ringfits = pd.read_table(ngradiifile,
comment='#',
header=0,
names=[
'age', 'flux', 'fluxerr',
'majorradius', 'majorradiuserr',
'minorradius', 'minorradiuserr',
'asymmetry', 'asymmetryerr', 'phi',
'phierr', 'chi2', 'dof'
],
index_col=False)
# columns: age(index) flux fluxerr majorradius majorradiuserr minorradius minorradiuserr asymmetry asymmetryerr phi phierr chi2 dof
if names == 'all' or 'ng2013radii' in names:
# plt.plot(ng2013_ringfits['age'],ng2013_ringfits[])
fancy_plot(np.array(ng2013_ringfits['age']),
np.array(ng2013_ringfits['majorradius']),
yerror=np.array(ng2013_ringfits['majorradiuserr']),
colors='black',
syms='x')
fancy_plot(np.array(ng2013_ringfits['age']),
np.array(ng2013_ringfits['minorradius']),
yerror=np.array(ng2013_ringfits['minorradiuserr']),
colors='black',
syms='x')
if names == 'all' or 'ng2013fluxes' in names:
ngradiifile = '~/Dropbox/Research/SN1987A/ng2013_ring_radii.txt'
ng2013_ringfits = pd.read_table(ngradiifile,
comment='#',
header=0,
names=[
'age', 'flux', 'fluxerr',
'majorradius', 'majorradiuserr',
'minorradius', 'minorradiuserr',
'asymmetry', 'asymmetryerr', 'phi',
'phierr', 'chi2', 'dof'
],
index_col=False)
# columns: age(index) flux fluxerr majorradius majorradiuserr minorradius minorradiuserr asymmetry asymmetryerr phi phierr chi2 dof
# plt.plot(ng2013_ringfits['age'],ng2013_ringfits[])
fancy_plot(np.array(ng2013_ringfits['age']),
np.array(ng2013_ringfits['flux'] / 3.0),
yerror=np.array(ng2013_ringfits['fluxerr']),
colors='black',
syms='x')
#--Initialize Plot--
ax1 = start_plot(xtitle='SN1987A Age [days]',
ytitle='Radius [arcsec]',
xmin=args.agemin,
xmax=args.agemax,
ymin=rmin,
ymax=rmax)
#----Plot----
fancy_plot(ax1,
fitdata['age'],
fitdata['R0'],
yerror=fitdata['R0errl'],
yerror_high=fitdata['R0erru'],
syms=syms,
colors=colors,
sizes=sizes,
alphas=alphas,
mecs=mecs,
mews=mews,
errorband=False)
#----Plot broken-linear fits----
if args.plotfit == 'yes':
for bi in range(len(bands)):
b = bands[bi]
#--read best-fit model file--
modeldata = np.genfromtxt(radiusfilename + '_' + b +
'_mpfit_radii.txt',
skip_header=0,
names=True,
broaddf = pd.concat([broaddf,obsinfo],join='inner',axis=1)
#-load data from the broken linear fit-
fitdf = pd.read_table('radii_fits_official_300-8000_mpfit_line.txt',sep='\t',comment='#',na_values=None)
#fit2df = pd.read_table('radii_fits_300-8000_300-8000_mpfit_line.txt',sep='\t',comment='#',na_values=None)
#fitdf = pd.read_table('radii_fits_official_300-8000_mpfit_line.txt',sep='\t',comment='#',na_values=None)
#-load data from Svet's density profile fit-
svetdf = pd.read_table('sn1987a_chandra_2016_density_expansion_fit.txt',header=6,sep=r'\s+',engine='python')
#-plot stuff-
pdffile=PdfPages('radius_vs_age_broad.pdf')
fig,ax=snplt.standard_age_plot(broaddf,'R0',ytitle='Radius [arcsec]',yerrlow='R0errl',yerrhigh='R0erru',errinterval=False,gratings='grating',ymin=ymin,ymax=ymax,agemin=agemin,agemax=agemax)
fancy_plot(ax,fitdf['age'],fitdf['radius'],colors='black',line='-',errorband=False,sizes=0.0000)
#fancy_plot(ax,fit2df['age'],fit2df['radius'],colors='black',line='--',errorband=False,sizes=0.0000)
#fancy_plot(ax,svetdf['age'],svetdf['fitradius'],colors='black',line='--',errorband=False,sizes=0.0000)
# legend
legsyms = ['s','d','o'] #square for each band, shape for each grating and for PN
leglabels = ['0.3-8 keV','ACIS (no grating)','ACIS (w/HETG)']
legcolors = ['black','white','white']
legmecs = ['black','black','black']
leg1 = snplt.plot_legend(ax,labels=leglabels,colors=legcolors,markers=legsyms,mecs=legmecs,fontsize='small',location='upper left')
# velocities annotation
v1 = 'v$_{early}='+str(int(round(broadparamdf['v_early'][0])))+'\pm'+str(int(round(broadparamdf['v_early'][1])))+'$ km s$^{-1}$'
v2 = 'v$_{late}~='+str(int(round(broadparamdf['v_late'][0])))+'\pm'+str(int(round(broadparamdf['v_late'][1])))+'$ km s$^{-1}$'
ax.annotate(v1+'\n'+v2,xycoords='figure fraction',textcoords='figure fraction',xy=(0.9,0.1),xytext=(0.5,0.22))
fax[1, 1].plot(t_mat[t_mat['By'] > -9990.0].index,
t_mat[t_mat['By'] > -9990.0].By,
color=color[trainer],
linewidth=2)
if len(t_mat[t_mat['Bz'] > -9990.0]) > 0:
fax[2, 1].scatter(t_mat[t_mat['Bz'] > -9990.0].index,
t_mat[t_mat['Bz'] > -9990.0].Bz,
marker=marker[trainer],
color=color[trainer])
fax[2, 1].plot(t_mat[t_mat['Bz'] > -9990.0].index,
t_mat[t_mat['Bz'] > -9990.0].Bz,
color=color[trainer],
linewidth=2)
fancy_plot(fax[0, 0])
fancy_plot(fax[1, 0])
fancy_plot(fax[2, 0])
fancy_plot(fax[0, 1])
fancy_plot(fax[1, 1])
fancy_plot(fax[2, 1])
i = pd.to_datetime("2016/12/21 08:43:12")
offset = pd.to_timedelta('30 minutes')
fax[0, 0].set_ylabel('Np [cm$^{-3}$]', fontsize=20)
fax[1, 0].set_ylabel('Th. Speed [km/s]', fontsize=20)
fax[2, 0].set_ylabel('Flow Speed [km/s]', fontsize=20)
fax[2, 0].set_xlabel('Time [UTC]', fontsize=20)
fax[0, 1].set_ylabel('Bx [nT]', fontsize=20)
fax[1, 1].set_ylabel('By [nT]', fontsize=20)
ax[0].plot(n.med_l, n.dis, color=d[1], linestyle=d[3], label=d[4])
ax[1].plot(-s.med_l, 1. - s.dis, color=d[1], linestyle=d[3], label=d[4])
ax2[j].plot(np.abs(n.med_l), n.dis, color='red', label='Nothern')
ax2[j].plot(np.abs(s.med_l),
1. - s.dis,
color='black',
linestyle='--',
label='Southern')
if ad[-1] < 1.0:
ax2[j].text(5, .8, 'p(A-D) = {0:5.4f}'.format(ad[-1]), fontsize=18)
ax2[j].text(5, .75, 'p(KS2) = {0:5.4f}'.format(k2[-1]), fontsize=18)
ax2[j].set_title(d[4])
ax2[j].set_xlabel(r"$|$Med. Latitude$|$ [Deg.]")
ax2[j].set_xlim([0, 90])
fancy_plot(ax2[j])
#do the camparision for stable filaments vs no stable
if i == 'fil1':
#setup for combined 1 and 2 categories
d = fil_dict['fil12']
d[0] = d[0][~d[0].index.duplicated(keep='first')]
n = d[0][d[0].north == 1]
s = d[0][d[0].north == 0]
n = setup_dis(n)
s = setup_dis(s)
#setup for combined 1, 2, and 3 categories
e = fil_dict['fil123']
e[0] = e[0][~e[0].index.duplicated(keep='first')]
def plot_filament_track(i):
global dat
dpi = 200
good, = np.where(dat['track_id'].values == i)
#get start time of track for filename
ofname = '{0}_track{1:6d}'.format(dat['event_starttime'][good[0]],
i).replace(' ', '0').replace(':', '_')
#create figure and add sun
fig, ax = plt.subplots(figsize=(7, 7), dpi=dpi)
rs = plt.Circle((0., 0.),
radius=1000.,
color='gray',
fill=False,
linewidth=5,
zorder=0)
ax.add_patch(rs)
#plot track polygons for given id
for j in good:
inc = 'red'
if dat['obs_observatory'].values[j] == 'HA2': inc = 'blue'
poly = plt.Polygon(loads(dat['hpc_bbox'].values[j]).exterior,
color=inc,
linewidth=0.5,
fill=None)
ax.add_patch(poly)
#over plot rotation track
#ax = plot_rotation(dat['hpc_coord'].values[j],dat['event_starttime_dt'][j],dat['event_endtime_dt'][j],dat['track_id'].values[j],ax,linestyle='--',dh=12,color='red')
#find other nearby events in time
nearbyh = 12. # hours
nearbya = 10. # arcsec
nearstartt = np.abs(dat['event_starttime_dt'][good[0]] -
dat['event_endtime_dt']) <= datetime.timedelta(
hours=nearbyh)
nearendt = np.abs(dat['event_endtime_dt'][good[-1]] -
dat['event_starttime_dt']) <= datetime.timedelta(
hours=nearbyh)
nearstartp = np.sqrt(
(dat['meanx'].values[good[0]] - dat['meanx'].values)**2 +
(dat['meany'].values[good[0]] - dat['meany'].values)**2) <= nearbya
nearendp = np.sqrt(
(dat['meanx'].values[good[-1]] - dat['meanx'].values)**2 +
(dat['meany'].values[good[-1]] - dat['meany'].values)**2) <= nearbya
#: nearby, = np.where((nearstartt & nearstartp) | (nearendt & nearendp))
nearby, = np.where((nearendp & nearendt))
print dat['meanx'].values[good[-1]]
print dat['meanx'][nearby]
#plot within time tracks
for j in nearby:
poly = plt.Polygon(loads(dat['hpc_bbox'].values[j]).exterior,
color='black',
linewidth=0.5,
fill=None)
ax.add_patch(poly)
ax = plot_rotation(dat['hpc_coord'].values[j],
dat['event_starttime_dt'][j],
dat['event_endtime_dt'][j],
dat['track_id'].values[j],
ax,
dh=48)
#Setup plots
fancy_plot(ax)
ticks = [-1000., -500., 0., 500., 1000.]
lim = [-1200., 1200.]
ax.set_xlim(lim)
ax.set_ylim(lim)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xlabel('Solar X [arcsec]')
ax.set_ylabel('Solar Y [arcsec]')
#save fig
fig.savefig('track_plots/' + ofname,
bbox_pad=.1,
bbox_inches='tight',
dpi=dpi)
fig.clear()
plt.close()
return
fax[0, 0].set_xlim(
[i - pd.to_timedelta('25 minutes'), i + pd.to_timedelta('25 minutes')])
fax[0, 0].set_ylabel('Np [cm$^{-3}$]', fontsize=20)
fax[1, 0].set_ylabel('Th. Speed [km/s]', fontsize=20)
fax[2, 0].set_ylabel('Flow Speed [km/s]', fontsize=20)
fax[2, 0].set_xlabel('Time [UTC]', fontsize=20)
fax[0, 1].set_ylabel('Bx [nT]', fontsize=20)
fax[1, 1].set_ylabel('By [nT]', fontsize=20)
fax[2, 1].set_ylabel('Bz [nT]', fontsize=20)
fax[2, 1].set_xlabel('Time [UTC]', fontsize=20)
#make fancy plot
for pax in fax.ravel():
fancy_plot(pax)
#ax.set_ylim([300,1000.])
bfig.savefig(
'../plots/spacecraft_events/event_{0:%Y%m%d_%H%M%S}_zoom.png'.format(
i.to_pydatetime()),
bbox_pad=.1,
bbox_inches='tight')
fax[0, 0].set_xlim([
i - pd.to_timedelta('180 minutes'), i + pd.to_timedelta('180 minutes')
])
bfig.savefig(
'../plots/spacecraft_events/event_{0:%Y%m%d_%H%M%S}_bigg.png'.format(
i.to_pydatetime()),
bbox_pad=.1,
bbox_inches='tight')
def standard_age_plot(df,
y,
ages='age',
gratings=None,
simoffsets=None,
agemin=4400,
agemax=10600,
ymin=0.0,
ymax=None,
plotextras=None,
clean=True,
color='black',
overplot=False,
ax=None,
symsize=8,
ytitle=None,
sym='o',
yerrlow=None,
yerrhigh=None,
errinterval=True,
ylog=False,
errorband=False,
line='',
linewidth=1,
figsize=None,
mews=None,
mecs=None,
alphas=1.0,
mealphas=None,
linecolor=None,
**kwargs):
"""
Create a standard plot of age vs Y for SN1987A data
Author: Kari A. Frank
Date: 2015-12-18
Input
df : pd.DataFrame
dataframe containing columns for, at a minimum, observation ages and
y values
ages : string
name of the column in df containing the observation ages
y : string
name of the column in df containing the y values to be plotted
yerrlow, yerrhigh : string
name of the columns containing the upper and lower errors on y
errinterval : bool
if True, then assume errors are the lower and upper endpoints of the
error interval and convertthem to error bars before plotting.
gratings : string
name of the column in df specifying the gratings used for each
observation represented in x (and y). if provided, each grating
type will be plotted with a different symbol
simoffsets : string
name of column in df specifying detector sim offsets for each
observation. if provided, the symbol outlines for these
observations will be gray instead of the usual black
agemin,agemax : numerical
minimum and maximum ages, in days for the x-axis
ymin, ymax : numerical
optionally specify the minimum and/or maximum values for the y-axis
plotextras : string
comma separated list of names for the extras to plot (passed directly
to plot_extras)
clean : bool
if False, will plot x ticks and a vertical line across the plot at
every data point to more easily associate specific observations with
a specific data point
color : string
specify a color for all the points. to plot with more than one color,
call this function again with different y and overplot=True.
overplot : bool
if True, will skip creating a new figure and just plot the
provided x and y on the current figure. must also provide
the ax argument.
ax : axis object
used only if overplot=True, to make sure it overplotted on
the correct axis
sym : string
specify the symbol to use (will be ignored if gratings is given)
symsize : numerical
specify the size of the symbols (default 8 is good for
presentations, use 4 to more clearly see error bars)
ylog : bool
plot the y axix on log scale if True (default=False)
errorband : bool
if True will plot the errors as a shaded band rather than bars
(passed directly to fancy_plot). default=False
line : string
format of the line to connect data points. same as in pyplot.plot
and fancy_plot. default = '' (no line)
linewidth : numeric
width of the line to connect data points. same as in pyplot.plot
and fancy_plot. default = 1
linecolor : string
color of the line to connect data points. same as in pyplot.plot
and fancy_plot. default = None
figsize : 2-element numerical tuple
tuple of the form (5,3) to specify the size of the plot in
inches (x,y)
mews : numerical
passed to fancy_plot to set the markeredge width
mecs : string
passed to fancy_plot to set the markeredge color
alphas : numerical
passed directly to fancy_plot to set the symbol alpha
mealphas : numerical
passed directly to fancy_plot to set the markeredge alpha. default
is to be the same as the marker (face) alpha
Notes
- In future, may add ability to also specify plotting a second
column as y values, optionally with a different color and symbols
- To save the plot to a pdf file, initialize the pdffile before calling
this function, e.g.:
from matplotlib.backends.backend_pdf import PdfPages
pdffile = PdfPages(plotfile)
and close it afterwards:
pdffile.savefig()
plt.close()
pdffile.close()
"""
#----Set up symbols----
if gratings is not None:
df['symbols'] = 'd'
# change according to grating
df.loc[(df[gratings].values == 'HETG'), 'symbols'] = 'o'
df.loc[(df[gratings].values == 'LETG'), 'symbols'] = '*'
#----Set up marker edge colors and widths----
if mews is None:
df['mews'] = 0.5
else:
df['mews'] = mews
if mecs is None:
df['mecs'] = color
else:
df['mecs'] = mecs
if mealphas is None:
mealphas = alphas
# if simoffsets is not None:
# # make sure column is strings
# df[simoffsets] = pd.Series([str(off) for off in df[simoffsets]])
# df['mecs'][df[simoffsets] == '-8.42'] = 'gray'
# mecs = df['mecs'].values
#----Set plot fonts----
set_plot_fonts()
#----Initialize Plot----
if overplot is False:
if ytitle is None:
ytitle = y # label it with the column name
fig, ax1 = start_plot(xtitle='SN1987A Age [days]',
ytitle=ytitle,
xmin=agemin,
xmax=agemax,
ymin=ymin,
ymax=ymax,
ylog=ylog,
clean=clean,
figsize=figsize)
else:
ax1 = ax
#----Drop rows with missing y data----
df = df.dropna(subset=[y])
if gratings is not None:
sym = df['symbols'].values
#----Convert Error Intervals to Error Bars----
if errinterval is True:
if yerrlow is not None:
yerrlow = df[y] - df[yerrlow]
if yerrhigh is not None:
yerrhigh = df[yerrhigh] - df[y]
else:
if yerrlow is not None:
yerrlow = df[yerrlow]
if yerrhigh is not None:
yerrhigh = df[yerrhigh]
#----Plot the data----
fancy_plot(ax1,
df[ages],
df[y],
yerror=yerrlow,
yerror_high=yerrhigh,
syms=sym,
mecs=df['mecs'].values,
mews=df['mews'].values,
line=line,
linewidth=linewidth,
errorband=errorband,
colors=color,
sizes=symsize,
linealpha=1.0,
bandalpha=0.1,
alphas=alphas,
ealphas=mealphas,
mealphas=mealphas)
#----Plot Extras----
if plotextras is not None:
plot_extras(names=plotextras)
#----Plot Legend----
# if overplotting, then need to plot the legend separately after all
# calls to standard_age_plot
# plot_legend(labels=leglabels,colors=legcolors,markers=legsyms,mecs=legmecs,
# mews=legmews,fontsize='small')
#----Plot Top Year Axis---
if overplot is False:
top_year_axis(ax1, agemin=agemin, agemax=agemax)
#----Return----
if overplot is False:
return fig, ax1
else:
return None
def test_dtw_example():
"""
Test using code
"""
#Wind to get DTW solution
window = pd.to_timedelta(1. * 3600., unit='s')
#Setup format for datetime string to pass to my_dtw later
dfmt = '{0:%Y/%m/%d %H:%M:%S}'
#twind4 = pd.to_datetime('2016/12/09 04:45:29')
#start_t4 = dfmt.format(twind4-2.5*window)
#end_t4 = dfmt.format(twind4+3.5*window)
twind4 = pd.to_datetime('2016/12/21 08:43:12')
start_t4 = dfmt.format(twind4 - 2.5 * window)
end_t4 = dfmt.format(twind4 + 3.5 * window)
#my_dtw4 = mtr.dtw_plane(start_t4,end_t4,nproc=4,penalty=True,events=7,earth_craft=['THEMIS_B'],par=['Bt'],speed_pen=500,mag_pen=100.2)
my_dtw4 = mtr.dtw_plane(start_t4,
end_t4,
nproc=4,
penalty=True,
events=7,
par=['Bt'],
earth_craft=['THEMIS_B'],
speed_pen=500,
mag_pen=100.2)
my_dtw4.init_read()
my_dtw4.iterate_dtw()
#my_dtw4.pred_earth()
#mtr.omni_plot(my_dtw4)
sc1 = 'Wind'
sc2 = 'SOHO'
x1 = np.array(my_dtw4.plsm[sc1].SPEED.ffill().bfill().values,
dtype=np.double)
x2 = np.array(my_dtw4.plsm[sc2].SPEED.ffill().bfill().values,
dtype=np.double)
#Example DTW plot
p1, p2, cost1 = md.dtw_path_single(x1, x2, 300, 30, 500.0, 0.0, 0.5, 1)
#p1,p2,cost = md.dtw_path_single(x2,x2],2700,2700/2,0.0,0.01,1)
#mlpy example path
dist, costa, path = mlpy.dtw_std(x1, x2, dist_only=False)
pa, pb = path[0], path[1]
#create multi panel diagnostic plot 2018/11/26 J. Prchlik
fig, ax = plt.subplots(nrows=2,
ncols=2,
gridspec_kw={
'height_ratios': [2, 1],
'width_ratios': [1, 2]
},
figsize=(8, 8))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
#turn off bottom left axis
ax[1, 0].axis('off')
lims = mdates.date2num([
my_dtw4.plsm[sc1].index.min(), my_dtw4.plsm[sc1].index.max(),
my_dtw4.plsm[sc2].index.min(), my_dtw4.plsm[sc2].index.max()
])
v_max, v_min = np.percentile(costa, [95, 15])
ax[0, 1].imshow(costa,
extent=lims,
origin='lower',
cmap=plt.cm.gray.reversed(),
vmin=v_min,
vmax=v_max,
aspect='auto')
#ax.imshow(cost,extent=[0,x2.size,0,x2[::2].size],origin='lower')
ax[0, 1].plot(my_dtw4.plsm[sc1].iloc[p1, :].index,
my_dtw4.plsm[sc2].iloc[p2, :].index,
'--',
color='black')
ax[0, 1].plot(my_dtw4.plsm[sc1].iloc[pa, :].index,
my_dtw4.plsm[sc2].iloc[pb, :].index,
'-',
color='red')
ax[0, 1].xaxis_date()
ax[0, 1].yaxis_date()
date_format = mdates.DateFormatter('%H:%M')
#plot the plasma values on the off axes
ax[1, 1].plot(my_dtw4.plsm[sc1].index, x1, color='blue')
ax[0, 0].plot(x2, my_dtw4.plsm[sc2].index, color='teal')
#set up axis formats
ax[1, 1].xaxis_date()
ax[0, 0].yaxis_date()
#force limits to be the same as the cost matrxi
ax[1, 1].set_xlim(lims[:2])
ax[0, 0].set_ylim(lims[2:])
#Format the printed dates
ax[1, 1].xaxis.set_major_formatter(date_format)
ax[0, 0].yaxis.set_major_formatter(date_format)
#Add label time
ax[1, 1].set_xlabel(sc1 + ' Time [UTC]')
ax[0, 0].set_ylabel(sc2 + ' Time [UTC]')
#Add label for Speeds
ax[1, 1].set_ylabel('Flow Speed [km/s]')
ax[0, 0].set_xlabel('Flow Speed [km/s]')
#turn off y-tick labels in center plot
ax[0, 1].set_xticklabels([])
ax[0, 1].set_yticklabels([])
#set Wind and SOHO to have the same plasma paramter limits
pls_lim = [420., 675.]
ax[0, 0].set_xlim(pls_lim)
ax[1, 1].set_ylim(pls_lim)
#copy y-axis labels from Wind plot to SOHO plot
ax[0, 0].set_xticks(ax[1, 1].get_yticks())
ax[0, 0].set_xlim(pls_lim)
ax[1, 1].set_ylim(pls_lim)
##ax[0,0].set_xlabel('Flow Speed [km/s]')
#clean up the axes with plasma data
fancy_plot(ax[0, 0])
fancy_plot(ax[1, 1])
# This simply sets the x-axis data to diagonal so it fits better.
#fig.autofmt_xdate()
fig.savefig('../plots/example_dtw_path.png',
bbox_pad=.1,
bbox_inches='tight')
fig.savefig('../plots/example_dtw_path.eps',
bbox_pad=.1,
bbox_inches='tight')
return x1, x2, my_dtw4
def create_fc_grid_plot(fcs,waeff=3.8e6,log_plot=False,ylim=None):
"""
Plot multiple FC on one grid of plots
Parameters
----------
fcs: dictionary
Dictionary of FC measurements
waeff: float, optional
Effective array of FC (Default = 3.8e6 #cm^3/km, Wind). This only comes into effect
if a 'cont' keyword does not exist in the fcs dictionary. The 'cont' keyword will exist if
read in the observations using the read_fmt_obs module.
log_plot: boolean
Plot the velocity distributions in log space (Default = False)
ylim: list
A two value list to set the plot limits (Default = None). The first component is
the minimum value, while the last component is the maximum value.
Return
-------
fig, ax: Figure Object, Axis Object
The created plots figure and axis objects
"""
#elemetary charge
q0 = 1.6021892e-7 # picocoulombs
#create a square as possible plot
nrows = int(np.sqrt(len(fcs.keys())))
ncols = len(fcs.keys())/nrows
#add an extra column if needed
if nrows*ncols < len(fcs.keys()):
ncols += 1
#create figure to plot
fig, axs = plt.subplots(ncols=ncols,nrows=nrows,sharex=True,sharey=True,figsize=(3*ncols,3*nrows))
fig.subplots_adjust(wspace=0.01,hspace=0.01)
counter = 0
for k,ax in zip(range(len(fcs.keys())),axs.ravel()):
key = 'fc_{0:1d}'.format(k)
#first column of x_measure is the velocity grid
grid_v = fcs[key]['x_meas'][0,:]
dv = fcs[key]['x_meas'][1,:]
#check to see if a constant value is already defined fcs dictionary
if 'cont' in fcs[key]:
#Switching to constant value that is in the fcs dictionary
cont = fcs[key]['cont'].ravel()
#Otherwise set a constant value equal using the constant effective area used above
else:
cont = 1.e12/(waeff*q0*dv*grid_v)
#sort by the velocity values when plotting
sort = np.argsort(grid_v)
#removed to test improving Gaussian fit
#add checks to plot only variables that are defined
#Plot best fit model
if 'dis_cur' in fcs[key]:
ax.plot(grid_v[sort],fcs[key]['dis_cur'].ravel()[sort]*cont,label='Best Mod.',color='black',linewidth=3)
#Plot measurements from the FC
if 'rea_cur' in fcs[key]:
ax.plot(grid_v[sort].ravel(),fcs[key]['rea_cur'].ravel()[sort]*cont,'-.b',label='Input',linewidth=3)
#plot error in measurements
if 'unc' in fcs[key]:
ax.errorbar(grid_v[sort],fcs[key]['rea_cur'].ravel()[sort]*cont,yerr=fcs[key]['unc'][sort]*cont,fmt='o',label=None,color='blue')
#ax.plot(grid_v,rea_cur.ravel()*cont,'-.b',label='Input',linewidth=3)
if 'init_guess' in fcs[key]:
ax.plot(grid_v[sort],fcs[key]['init_guess'].ravel()[sort]*cont,':',color='purple',label='Init. Guess',linewidth=3)
ax.text(0.05,0.8,'$\Phi$={0:2.1f}$^\circ$\n$\Theta$={1:2.1f}$^\circ$'.format(*np.degrees(fcs[key]['x_meas'][[2,3],0])),transform=ax.transAxes)
#ax.plot(grid_v, gaus(grid_v,*popt),'--',marker='o',label='Gauss Fit',linewidth=3)
fancy_plot(ax)
#set the scale to log if log keyword set
if log_plot:
ax.set_yscale('log')
#set the y-limit to the ylim if the value is set
if ylim is not None:
ax.set_ylim(ylim)
#only plot x-label if in the last row
if counter >= (nrows-1)*(ncols):
ax.set_xlabel('Speed [km/s]')
#set ylabel on the left edge
if np.isclose(float(counter)/ncols - int(counter/ncols),0):
ax.set_ylabel('p/cm$^{-3}$/(km/s)')
#put legend only on the first plot
if counter == 0:
ax.legend(loc='best',frameon=False)
counter += 1
#Need to put this outside loop
#pad 20% x-lim for angle labels on plot
x_lim = np.array(ax.get_xlim())
x_lim[0] -= 0.2*(x_lim[1]-x_lim[0])
ax.set_xlim(x_lim)
return fig,axs
fig, ax = snplt.standard_age_plot(broaddf,
'R0',
ytitle='Radius [arcsec]',
yerrlow='R0errl',
yerrhigh='R0erru',
errinterval=False,
gratings='grating',
ymin=ymin,
ymax=ymax,
agemin=agemin,
agemax=agemax)
fancy_plot(ax,
fitdf['age'],
fitdf['radius'],
colors='black',
line='-',
errorband=False,
sizes=0.0000)
#fancy_plot(ax,fit2df['age'],fit2df['radius'],colors='black',line='--',errorband=False,sizes=0.0000)
#fancy_plot(ax,svetdf['age'],svetdf['fitradius'],colors='black',line='--',errorband=False,sizes=0.0000)
# legend
legsyms = ['s', 'd',
'o'] #square for each band, shape for each grating and for PN
leglabels = ['0.3-8 keV', 'ACIS (no grating)', 'ACIS (w/HETG)']
legcolors = ['black', 'white', 'white']
legmecs = ['black', 'black', 'black']
leg1 = snplt.plot_legend(ax,
labels=leglabels,
def plot_extras(names='all',agemin=4000,agemax=11000):
"""
Author: Kari A. Frank
Date: September 22, 2015
Purpose: Plot lines on already open age vs Y plots (where Y is
typically either radius or flux
Input:
names : string
comma separated list of names of which extras to plot.
default = 'all'. options are:
'plait1995'- plots position and width of the ER from
Plait1995 (in this case Y must be radius)
'ng2013' - plots the radio break age day=7600 from the
Ng2013 torus fits to radio images, as vertical line
'sugarman2002' - plots location of the ring from Sugarman 2002
(in this case Y must be radius)
'hotspots' - plots the radius and day of appearance for the
HST hot spots in Sugarman 2002
'larsson2014' - plots the age of transition from radioactive
decay to X-ray heating as inner debris energy source
'chandrabroadbreak' - plot as vertical line the best fit
age of the break point in expansion curve
'plaitwidth' - plot distance from chandrabroadbreak radius
to width of plait ring
Output:
- plots to an already open plot
Usage Notes:
- will not add anything to the plot legend
"""
dummy_y = [0.00000001,1000.] #dummy y values for vertical lines
dummy_sym = '.'
if names == 'all' or 'hotspots' in names:
# hot spot appearances in HST, Sugarman2002 table 1
hotspot_ages = [2933,4283,4337,4337,4440,4440,4440,4725,
4816,4816,4999,4999]
hotspot_radii = [0.56,0.673,0.607,0.702,0.565,0.697,0.707,
0.607,0.535,0.629,0.531,0.713]
fancy_plot(hotspot_ages,hotspot_radii,syms='*',
colors='white')
# legcolors+=['white'] #marker will look like border only
# leglabels+=['HST Hot Spots']
# legmecs+=['black']
# legmews+=[0.5]
# legsyms+=['*']
if names == 'all' or 'larsson2014' in names:
#Larsson2013 -- inner debris (HST) morphology transitioned
# from core to edge-brightened due to
# energy source shifting from radioactive decay to X-ray
# illumination
debris_transition_age = [5500,5500]
debris_transition_radius = dummy_y#plot vertical line
fancy_plot(debris_transition_age,debris_transition_radius,
colors='black',line='--',syms=dummy_sym)
#legcolors+=[None] #marker will look like border only
#leglabels+=['Debris Transition']
#legmecs+=[None]
#legmews+=[0.]
#legsyms+=[None]
if names == 'all' or 'plait1995' in names:
# Width and location of optical ring from UV flash,
# Plait1995
# plot horizontal band
plaitwidth = np.array([0.121/2.0,0.121/2.0])
plait_radii = [0.86,0.86]
plait_ages = [agemin,agemax]
fancy_plot(plait_ages,plait_radii,colors='gray',
syms=dummy_sym,errorband=True,yerror=plaitwidth)
if names == 'all' or 'plaitwidth' in names:
# width of Plait ring, from chandra break point
plaitwidth = np.array([0.121/2.0,0.121/2.0])
plait_ages = [-1.0,20000.0]
chandrabreakradii = [0.73+0.121/2.0,0.73+0.121/2.0]
fancy_plot(plait_ages,chandrabreakradii,colors='purple',
syms=dummy_sym,errorband=True,yerror=plaitwidth)
if names == 'all' or 'sugarman2002' in names:
# Location of ring from Sugarman2002 (table 3)
sugarman_radii = [0.829,0.829]
sugarman_ages = [agemin,agemax]
fancy_plot(plait_ages,plait_radii,colors='gray',
syms=dummy_sym,errorband=True,yerror=plaitwidth)
if names == 'all' or 'ng2013' in names:
#Ng2013 Radio expansion measurements: using the torus
# model found the following all around day 7600:
# - break in expansion (decrease in velocity)
# - break in torus opening angle, from constant ~40deg
# to decreasing
# - break in asymmetry; the morphology was ~40% asymmetric,
# but became steadily more symmetric at the break
# - break in the light curve; the slope flattened,
# deviating from previous exponential growth
radiobreak_radii = dummy_y #plot vertical line
radiobreak_age = [7600,7600]
fancy_plot(radiobreak_age,radiobreak_radii,syms=dummy_sym,
colors='black',line=':')
if names == 'all' or 'chandrabroadbreak' in names:
# plot best fit age of the break in expansion from the
# 300-8000 chandra images
chandrabreak_radii = dummy_y #plot vertical line
chandrabreak_age = [5962.77,5962.77]
fancy_plot(chandrabreak_age,chandrabreak_radii,
syms=dummy_sym,colors='black',line='--')
if (names == 'all' or 'ng2013radii' in names or 'ng2013fluxes'
in names):
ngradiifile = ('/astro/research/kaf33/Dropbox/Research/SN1987A'
'/ng2013_ring_radii.txt')
ng2013_ringfits = pd.read_table(ngradiifile,comment='#',
header=0,names=
['age','flux','fluxerr','majorradius',
'majorradiuserr','minorradius',
'minorradiuserr','asymmetry',
'asymmetryerr','phi','phierr','chi2',
'dof'],index_col=False)
# columns: age(index) flux fluxerr majorradius majorradiuserr minorradius minorradiuserr asymmetry asymmetryerr phi phierr chi2 dof
if names == 'all' or 'ng2013radii' in names:
# plt.plot(ng2013_ringfits['age'],ng2013_ringfits[])
fancy_plot(np.array(ng2013_ringfits['age']),
np.array(ng2013_ringfits['majorradius']),
yerror=np.array(ng2013_ringfits['majorradiuserr']),
colors='black',syms='x')
fancy_plot(np.array(ng2013_ringfits['age']),
np.array(ng2013_ringfits['minorradius']),
yerror=np.array(ng2013_ringfits['minorradiuserr']),
colors='black',syms='x')
if names == 'all' or 'ng2013fluxes' in names:
ngradiifile = '~/Dropbox/Research/SN1987A/ng2013_ring_radii.txt'
ng2013_ringfits = pd.read_table(ngradiifile,comment='#',
header=0,names=
['age','flux','fluxerr','majorradius',
'majorradiuserr','minorradius',
'minorradiuserr','asymmetry',
'asymmetryerr','phi','phierr','chi2',
'dof'],index_col=False)
# columns: age(index) flux fluxerr majorradius majorradiuserr minorradius minorradiuserr asymmetry asymmetryerr phi phierr chi2 dof
# plt.plot(ng2013_ringfits['age'],ng2013_ringfits[])
fancy_plot(np.array(ng2013_ringfits['age']),
np.array(ng2013_ringfits['flux']/3.0),
yerror=np.array(ng2013_ringfits['fluxerr']),
colors='black',syms='x')
shock_df['counts'] = 1
#Resample at one month using the mean
df_bin = shock_df.resample('A').sum()
df_spb = sunsp_df.resample('A').sum()
df_cme = cmect_df.resample('A').sum()
fig, ax = plt.subplots(figsize=(24, 8), ncols=2)
ax[0].scatter(df_bin.index[1:], df_bin.counts[1:], color='black')
ax[0].scatter(df_cme.index, df_cme.counts, color='red', marker='D')
ax[0].scatter(df_cme.index, df_cme.h_cnts, color='blue', marker='s')
ax[0].plot(df_spb.index[1:], df_spb.SN[1:] * 2., color='black')
ax[0].set_ylabel('Number of Shock Events')
ax[0].set_xlabel('Year')
fancy_plot(ax[0])
ax[0].set_xlim([datetime(1995, 1, 1), datetime(2017, 1, 1)])
ax[1].scatter(df_bin.counts[1:], df_spb.SN[1:], color='black')
ax[1].scatter(df_bin.counts[1:], df_cme.counts, color='red', marker='D')
ax[1].scatter(df_bin.counts[1:], df_cme.h_cnts, color='blue', marker='s')
ax[1].set_xlabel('Number of Shock Events')
ax[1].set_ylabel('Number of Sun Spots')
r, p = pearsonr(df_bin.counts[1:], df_cme.h_cnts)
print r, p
ax[1].text(3000., 2000., 'r={0:4.3f}'.format(r))
fancy_plot(ax[1])
plt.show()
def standard_age_plot(df,y,ages='age',gratings=None,simoffsets=None,
agemin=4400,agemax=10600,ymin=0.0,ymax=None,
plotextras=None,clean=True,color='black',
overplot=False,ax=None,symsize=8,ytitle=None,
sym='o',yerrlow=None,yerrhigh=None,errinterval=True,
ylog=False,errorband=False,line='',linewidth=1,
figsize=None,mews=None,mecs=None,alphas=1.0,
mealphas=None,linecolor=None,
**kwargs):
"""
Create a standard plot of age vs Y for SN1987A data
Author: Kari A. Frank
Date: 2015-12-18
Input
df : pd.DataFrame
dataframe containing columns for, at a minimum, observation ages and
y values
ages : string
name of the column in df containing the observation ages
y : string
name of the column in df containing the y values to be plotted
yerrlow, yerrhigh : string
name of the columns containing the upper and lower errors on y
errinterval : bool
if True, then assume errors are the lower and upper endpoints of the
error interval and convertthem to error bars before plotting.
gratings : string
name of the column in df specifying the gratings used for each
observation represented in x (and y). if provided, each grating
type will be plotted with a different symbol
simoffsets : string
name of column in df specifying detector sim offsets for each
observation. if provided, the symbol outlines for these
observations will be gray instead of the usual black
agemin,agemax : numerical
minimum and maximum ages, in days for the x-axis
ymin, ymax : numerical
optionally specify the minimum and/or maximum values for the y-axis
plotextras : string
comma separated list of names for the extras to plot (passed directly
to plot_extras)
clean : bool
if False, will plot x ticks and a vertical line across the plot at
every data point to more easily associate specific observations with
a specific data point
color : string
specify a color for all the points. to plot with more than one color,
call this function again with different y and overplot=True.
overplot : bool
if True, will skip creating a new figure and just plot the
provided x and y on the current figure. must also provide
the ax argument.
ax : axis object
used only if overplot=True, to make sure it overplotted on
the correct axis
sym : string
specify the symbol to use (will be ignored if gratings is given)
symsize : numerical
specify the size of the symbols (default 8 is good for
presentations, use 4 to more clearly see error bars)
ylog : bool
plot the y axix on log scale if True (default=False)
errorband : bool
if True will plot the errors as a shaded band rather than bars
(passed directly to fancy_plot). default=False
line : string
format of the line to connect data points. same as in pyplot.plot
and fancy_plot. default = '' (no line)
linewidth : numeric
width of the line to connect data points. same as in pyplot.plot
and fancy_plot. default = 1
linecolor : string
color of the line to connect data points. same as in pyplot.plot
and fancy_plot. default = None
figsize : 2-element numerical tuple
tuple of the form (5,3) to specify the size of the plot in
inches (x,y)
mews : numerical
passed to fancy_plot to set the markeredge width
mecs : string
passed to fancy_plot to set the markeredge color
alphas : numerical
passed directly to fancy_plot to set the symbol alpha
mealphas : numerical
passed directly to fancy_plot to set the markeredge alpha. default
is to be the same as the marker (face) alpha
Notes
- In future, may add ability to also specify plotting a second
column as y values, optionally with a different color and symbols
- To save the plot to a pdf file, initialize the pdffile before calling
this function, e.g.:
from matplotlib.backends.backend_pdf import PdfPages
pdffile = PdfPages(plotfile)
and close it afterwards:
pdffile.savefig()
plt.close()
pdffile.close()
"""
#----Set up symbols----
if gratings is not None:
df['symbols'] = 'd'
# change according to grating
df.loc[(df[gratings].values=='HETG'),'symbols']='o'
df.loc[(df[gratings].values=='LETG'),'symbols']='*'
#----Set up marker edge colors and widths----
if mews is None:
df['mews'] = 0.5
else:
df['mews'] = mews
if mecs is None:
df['mecs'] = color
else:
df['mecs'] = mecs
if mealphas is None:
mealphas = alphas
# if simoffsets is not None:
# # make sure column is strings
# df[simoffsets] = pd.Series([str(off) for off in df[simoffsets]])
# df['mecs'][df[simoffsets] == '-8.42'] = 'gray'
# mecs = df['mecs'].values
#----Set plot fonts----
set_plot_fonts()
#----Initialize Plot----
if overplot is False:
if ytitle is None:
ytitle = y # label it with the column name
fig,ax1 = start_plot(xtitle = 'SN1987A Age [days]',ytitle=ytitle,
xmin=agemin,xmax=agemax,ymin=ymin,ymax=ymax,
ylog=ylog,clean=clean,figsize=figsize)
else:
ax1 = ax
#----Drop rows with missing y data----
df = df.dropna(subset=[y])
if gratings is not None:
sym = df['symbols'].values
#----Convert Error Intervals to Error Bars----
if errinterval is True:
if yerrlow is not None:
yerrlow = df[y] - df[yerrlow]
if yerrhigh is not None:
yerrhigh = df[yerrhigh] - df[y]
else:
if yerrlow is not None:
yerrlow = df[yerrlow]
if yerrhigh is not None:
yerrhigh = df[yerrhigh]
#----Plot the data----
fancy_plot(ax1,df[ages],df[y],yerror=yerrlow,yerror_high=yerrhigh,
syms=sym,mecs=df['mecs'].values,mews=df['mews'].values,
line=line,linewidth=linewidth,errorband=errorband,
colors=color,sizes=symsize,linealpha=1.0,bandalpha=0.1,
alphas=alphas,ealphas=mealphas,mealphas=mealphas)
#----Plot Extras----
if plotextras is not None:
plot_extras(names=plotextras)
#----Plot Legend----
# if overplotting, then need to plot the legend separately after all
# calls to standard_age_plot
# plot_legend(labels=leglabels,colors=legcolors,markers=legsyms,mecs=legmecs,
# mews=legmews,fontsize='small')
#----Plot Top Year Axis---
if overplot is False:
top_year_axis(ax1,agemin=agemin,agemax=agemax)
#----Return----
if overplot is False:
return fig,ax1
else:
return None
def plot_a(ax,age,flux,flux_low,flux_high,syms='o',colors='b',sizes=4,alphas=1,mecs='black'):
elow,ehigh = ebars(flux,flux_low,flux_high)
fancy_plot(age,flux,yerror=elow,yerror_high=ehigh,syms=syms,colors=colors,sizes=sizes,alphas=alphas,mecs=mecs)
alphas = [1.0]*nobs print 'Markers Defined' #----Set Up Plot File---- #pdffile = PdfPages(args.plotfile) #with PdfPages(args.plotfile) as pdffile: #----R0 vs age---- #--Initialize Plot-- ax1 = start_plot(xtitle='SN1987A Age [days]',ytitle='Radius [arcsec]',xmin=args.agemin,xmax=args.agemax,ymin=rmin,ymax=rmax) #----Plot---- fancy_plot(fitdata['age'],fitdata['R0'],yerror=fitdata['R0errl'],yerror_high=fitdata['R0erru'],syms=syms,colors=colors,sizes=sizes,alphas=alphas,mecs=mecs,mews=mews,errorband=False) #----Plot Legend---- plot_legend(labels=leglabels,colors=legcolors,markers=legsyms,mecs=legmecs,mews=legmews,fontsize='small') #--set top axis-- set_axis(ax1,x=topx,twin=True,title=toptitle,xlab=toplab) #--save and close plot-- plt.show() #pdffile.savefig() #plt.close() print 'Radius vs Age plotted' #---------------------------------------
