def plotSIFGI():
a = eqtools.EqdskReader(gfile='/afs/ipp-garching.mpg.de/home/i/ianf/codes/python/general/g33669.3000')
a.remapLCFS()
b = eqtools.AUGDDData(33669)
tok = TRIPPy.Tokamak(b)
cont = (pow(scipy.linspace(0,1,11),2)*(a.getFluxLCFS()-a.getFluxAxis())+a.getFluxAxis())
print(a.getFluxAxis())
print(a.getFluxLCFS())
print(cont)
temp = b.getMachineCrossSectionFull()
plotEq.plot(a,33669,lims=temp,contours=-1*cont)
GI = genGRW(tok)
r = GI.r()[0][-2:]
z = GI.r()[2][-2:]
print(r,z)
plt.plot(r,z,color='#0E525A',linewidth=2.)
SIF = genSIF(tok)
r = SIF.r()[0][-2:]
z = SIF.r()[2][-2:]
print(r,z)
plt.plot(r,z,color='#228B22',linewidth=2.)
plt.subplots_adjust(left=.2,right=.95)
plt.gca().set_xlim([scipy.nanmin(temp[0].astype(float)),scipy.nanmax(temp[0].astype(float))])
plt.gca().set_ylim([scipy.nanmin(temp[1].astype(float)),scipy.nanmax(temp[1].astype(float))])
plt.text(1.4,.9,'CSXR',fontsize=22,zorder=10)
plt.text(2.25,.1,'GI',fontsize=22,zorder=10)
limy = plt.gca().get_ylim()
limx = plt.gca().get_xlim()
plt.gca().text(1.0075*(limx[1]-limx[0])+limx[0],.97*(limy[1]-limy[0])+limy[0],str(33669),rotation=90,fontsize=14)
def get_hmf(dat, edat, vecs, nit=5, convergence=0.01):
"""
dat should have the shape Nobs,Npix
edat the same thing
vecs should have the shape (npix, ncomp)
returns the eigen vectors and the projections vector
"""
ncomp = vecs.shape[1]
ndat = len(dat)
npix = len(dat[0])
# a step
# As = np.matrix(np.zeros((ndat,ncomp)))
# Gs = np.matrix(vecs)
As = shared_zeros_matrix(ndat, ncomp)
Gs = copy_as_shared(vecs)
data_struct.dat = dat
data_struct.edat = edat
data_struct.Gs = Gs
data_struct.As = As
for i in range(nit):
deltas1 = mapper(doAstep, range(ndat))
deltas2 = mapper(doGstep, range(npix))
curconv = scipy.nanmax([scipy.nanmax(deltas1), scipy.nanmax(deltas2)])
print curconv
if curconv < convergence:
break
# orthogonalize
Gs, As = orthogonalize(Gs, As)
return Gs, As
def get_hmf(dat, edat, vecs, nit=5, convergence=0.01):
"""
dat should have the shape Nobs,Npix
edat the same thing
vecs should have the shape (npix, ncomp)
returns the eigen vectors and the projections vector
"""
ncomp = vecs.shape[1]
ndat = len(dat)
npix = len(dat[0])
# a step
# As = np.matrix(np.zeros((ndat,ncomp)))
# Gs = np.matrix(vecs)
As = shared_zeros_matrix(ndat, ncomp)
Gs = copy_as_shared(vecs)
data_struct.dat = dat
data_struct.edat = edat
data_struct.Gs = Gs
data_struct.As = As
for i in range(nit):
deltas1 = mapper(doAstep, range(ndat))
deltas2 = mapper(doGstep, range(npix))
curconv = scipy.nanmax([scipy.nanmax(deltas1), scipy.nanmax(deltas2)])
print curconv
if curconv < convergence:
break
# orthogonalize
Gs, As = orthogonalize(Gs, As)
return Gs, As
def plot_map(outdict, e, savename, fig1=None, m=None):
"""
This function will plot the output data in a scatter plot over a map of the
satellite path.
Args:
outdict (dict[str, obj]): Output dictionary from analyzebeacons.
e (dict[str, obj]): Output dictionary from ephem_doponly.
savename(:obj:`str`): Name of the file the image will be saved to.
fig1(:obj:'matplotlib figure'): Figure.
m (:obj:'basemap obj'): Basemap object
"""
t = outdict['time']
slat = e['site_latitude']
slon = e['site_longitude']
if fig1 is None:
fig1 = plt.figure()
plt.figure(fig1.number)
latlim = [math.floor(slat - 15.), math.ceil(slat + 15.)]
lonlim = [math.floor(slon - 15.), math.ceil(slon + 15.)]
if m is None:
m = Basemap(lat_0=slat,
lon_0=slon,
llcrnrlon=lonlim[0],
llcrnrlat=latlim[0],
urcrnrlon=lonlim[1],
urcrnrlat=latlim[1])
m.drawcoastlines(color="gray")
m.plot(slon, slat, "rx")
scat = m.scatter(e["sublon"](t),
e["sublat"](t),
c=outdict['rTEC'],
cmap='viridis',
vmin=0,
vmax=math.ceil(sp.nanmax(outdict['rTEC'])))
plt.title('Map of TEC Over Satellite Path')
cb = plt.colorbar(scat, label='rTEC in TECu')
fig1.savefig(savename, dpi=300)
#plt.draw()
scat = m.scatter(e["sublon"](t),
e["sublat"](t),
c=outdict['rTEC_amp'],
cmap='viridis',
vmin=0,
vmax=math.ceil(sp.nanmax(outdict['rTEC_amp'])))
plt.title('Map of TEC_Amp Over Satellite Path')
cb.set_clim(vmin=0, vmax=math.ceil(sp.nanmax(outdict['rTEC_amp'])))
cb.draw_all()
#plt.tight_layout()
figpath, name = os.path.split(savename)
savename = os.path.join(figpath, 'amp' + name)
fig1.savefig(savename, dpi=300)
plt.close(fig1)
def sky(self):
if self.skyid is None:
skymax = scipy.nanmax(self.varspec)
scimax = scipy.nanmax(self.current)
scimin = scipy.nanmin(self.current)
spec = self.varspec * scimax / skymax - abs(scimin)
self.skyid = len(self.axes.lines)
pylab.plot(self.wave, spec, color='green')
else:
self.axes.lines.remove(self.axes.lines[self.skyid])
self.skyid = None
pylab.draw()
def CenteredLagProduct(rawbeams,
numtype=sp.complex128,
pulse=sp.ones(14),
lagtype='centered'):
""" This function will create a centered lag product for each range using the
raw IQ given to it. It will form each lag for each pulse and then integrate
all of the pulses.
Inputs:
rawbeams - This is a NpxNs complex numpy array where Ns is number of
samples per pulse and Npu is number of pulses
N - The number of lags that will be created, default is 14.
numtype - The type of numbers used to create the data. Default is sp.complex128
lagtype - Can be centered forward or backward.
Output:
acf_cent - This is a NrxNl complex numpy array where Nr is number of
range gate and Nl is number of lags.
"""
N = len(pulse)
# It will be assumed the data will be pulses vs rangne
rawbeams = rawbeams.transpose()
(Nr, Np) = rawbeams.shape
# Make masks for each piece of data
if lagtype == 'forward':
arback = sp.zeros(N, dtype=int)
arfor = sp.arange(N, dtype=int)
elif lagtype == 'backward':
arback = sp.arange(N, dtype=int)
arfor = sp.zeros(N, dtype=int)
else:
# arex = sp.arange(0,N/2.0,0.5);
arback = -sp.floor(sp.arange(0, N / 2.0, 0.5)).astype(int)
arfor = sp.ceil(sp.arange(0, N / 2.0, 0.5)).astype(int)
# figure out how much range space will be kept
ap = sp.nanmax(abs(arback))
ep = Nr - sp.nanmax(arfor)
rng_ar_all = sp.arange(ap, ep)
# wearr = (1./(N-sp.tile((arfor-arback)[:,sp.newaxis],(1,Np)))).astype(numtype)
#acf_cent = sp.zeros((ep-ap,N))*(1+1j)
acf_cent = sp.zeros((ep - ap, N), dtype=numtype)
for irng, curange in enumerate(rng_ar_all):
rng_ar1 = int(curange) + arback
rng_ar2 = int(curange) + arfor
# get all of the acfs across pulses # sum along the pulses
acf_tmp = sp.conj(rawbeams[rng_ar1, :]) * rawbeams[rng_ar2, :] #*wearr
acf_ave = sp.sum(acf_tmp, 1)
acf_cent[irng, :] = acf_ave # might need to transpose this
return acf_cent
def get_hmf_smooth(dat, edat, vecs, nit=5, eps=0.01, convergence=0.01):
"""
dat should have the shape Nobs,Npix
edat the same thing
vecs should have the shape (npix, ncomp)
returns the eigen vectors and the projections vector
"""
ncomp = vecs.shape[1]
ndat = len(dat)
npix = len(dat[0])
As = shared_zeros_matrix(ndat, ncomp)
Gs = copy_as_shared(vecs)
Gsold = shared_zeros_matrix(Gs.shape[0], Gs.shape[1])
#arrays used for processing
data_struct.Gs = Gs
data_struct.Gsold = Gsold
data_struct.As = As
# input data
data_struct.dat = dat
data_struct.edat = edat
# parameters
data_struct.eps = eps
data_struct.ncomp = ncomp
data_struct.npix = npix
for i in range(nit):
# a step
deltas1 = mapper(doAstep, range(ndat))
data_struct.Gsold, data_struct.Gs = data_struct.Gs, data_struct.Gsold
# swapping variables, because we going to update Gs while still using
# original Gs from A step
# g step
deltas2 = mapper(doGstepSmooth, range(npix))
curconv = scipy.nanmax([scipy.nanmax(deltas1), scipy.nanmax(deltas2)])
print curconv
if curconv < convergence:
break
Gs, As = orthogonalize(Gs, As)
return Gs, As
def get_hmf_smooth(dat, edat, vecs, nit=5, eps=0.01, convergence=0.01):
"""
dat should have the shape Nobs,Npix
edat the same thing
vecs should have the shape (npix, ncomp)
returns the eigen vectors and the projections vector
"""
ncomp = vecs.shape[1]
ndat = len(dat)
npix = len(dat[0])
As = shared_zeros_matrix(ndat, ncomp)
Gs = copy_as_shared(vecs)
Gsold = shared_zeros_matrix(Gs.shape[0], Gs.shape[1])
# arrays used for processing
data_struct.Gs = Gs
data_struct.Gsold = Gsold
data_struct.As = As
# input data
data_struct.dat = dat
data_struct.edat = edat
# parameters
data_struct.eps = eps
data_struct.ncomp = ncomp
data_struct.npix = npix
for i in range(nit):
# a step
deltas1 = mapper(doAstep, range(ndat))
data_struct.Gsold, data_struct.Gs = data_struct.Gs, data_struct.Gsold
# swapping variables, because we going to update Gs while still using
# original Gs from A step
# g step
deltas2 = mapper(doGstepSmooth, range(npix))
curconv = scipy.nanmax([scipy.nanmax(deltas1), scipy.nanmax(deltas2)])
print curconv
if curconv < convergence:
break
Gs, As = orthogonalize(Gs, As)
return Gs, As
def CenteredLagProduct(rawbeams, numtype=sp.complex128, pulse=sp.ones(14), lagtype='centered'):
"""
This function will create a centered lag product for each range using the
raw IQ given to it. It will form each lag for each pulse and then integrate
all of the pulses.
Inputs:
rawbeams - This is a NpxNs complex numpy array where Ns is number of
samples per pulse and Npu is number of pulses
N - The number of lags that will be created, default is 14.
numtype - The type of numbers used to create the data. Default is sp.complex128
lagtype - Can be centered forward or backward.
Output:
acf_cent - This is a NrxNl complex numpy array where Nr is number of
range gate and Nl is number of lags.
"""
n_pulse = len(pulse)
# It will be assumed the data will be pulses vs range
rawbeams = rawbeams.transpose()
n_range = rawbeams.shape[0]
# Make masks for each piece of data
if lagtype == 'forward':
arback = sp.zeros(n_pulse, dtype=int)
arfor = sp.arange(n_pulse, dtype=int)
elif lagtype == 'backward':
arback = sp.arange(n_pulse, dtype=int)
arfor = sp.zeros(n_pulse, dtype=int)
else:
# arex = sp.arange(0,N/2.0,0.5);
arback = -sp.floor(sp.arange(0, n_pulse/2.0, 0.5)).astype(int)
arfor = sp.ceil(sp.arange(0, n_pulse/2.0, 0.5)).astype(int)
# figure out how much range space will be kept
a_p = sp.nanmax(abs(arback))
e_p = n_range - sp.nanmax(arfor)
rng_ar_all = sp.arange(a_p, e_p)
# wearr = (1./(N-sp.tile((arfor-arback)[:,sp.newaxis],(1,Np)))).astype(numtype)
#acf_cent = sp.zeros((ep-ap,N))*(1+1j)
acf_cent = sp.zeros((e_p-a_p, n_pulse), dtype=numtype)
for irng, curange in enumerate(rng_ar_all):
rng_ar1 = int(curange) + arback
rng_ar2 = int(curange) + arfor
# get all of the acfs across pulses # sum along the pulses
acf_tmp = sp.conj(rawbeams[rng_ar1, :])*rawbeams[rng_ar2, :]#*wearr
acf_ave = sp.sum(acf_tmp, 1)
acf_cent[irng, :] = acf_ave# might need to transpose this
return acf_cent
def print_verbose_message(self):
"""Method to print training statistics if Verbose is TRUE"""
# Memory usage (does not work in Windows)
# print('Peak memory usage: %.2f MB' % (resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / infer_platform() ))
# Variance explained
r2 = s.asarray(self.calculate_variance_explained(total=True)).mean(axis=0)
r2[r2<0] = 0.
print("- Variance explained: " + " ".join([ "View %s: %.2f%%" % (m,100*r2[m]) for m in range(self.dim["M"])]))
# Sparsity levels of the weights
W = self.nodes["W"].getExpectation()
foo = [s.mean(s.absolute(W[m])<1e-3) for m in range(self.dim["M"])]
print("- Fraction of zero weights: " + " ".join([ "View %s: %.0f%%" % (m,100*foo[m]) for m in range(self.dim["M"])]))
# Correlation between factors
Z = self.nodes["Z"].getExpectation()
Z += s.random.normal(s.zeros(Z.shape),1e-10)
r = s.absolute(corr(Z.T,Z.T)); s.fill_diagonal(r,0)
print("- Maximum correlation between factors: %.2f" % (s.nanmax(r)))
# Factor norm
bar = s.mean(s.square(Z),axis=0)
print("- Factor norms: " + " ".join([ "%.2f" % bar[k] for k in range(Z.shape[1])]))
# Tau
tau = self.nodes["Tau"].getExpectation()
print("- Tau per view (average): " + " ".join([ "View %s: %.2f" % (m,tau[m].mean()) for m in range(self.dim["M"])]))
print("\n")
def max_filter_bord(im, size=3):
"""The function performs a local max filter on a flat image. Border's
pixels are processed.
Args:
im: the image to process
size: the size in pixels of the local square window. Default value is 3.
Returns:
out: the filtered image
"""
## Get the size of the image
[nl, nc, d] = im.shape
## Get the size of the moving window
s = (size - 1) / 2
## Initialization of the output
out = sp.empty((nl, nc, d), dtype=im.dtype.name)
temp = sp.empty((nl + 2 * s, nc + 2 * s, d), dtype=im.dtype.name) # A temporary file is created
temp[0:s, :, :] = sp.NaN
temp[:, 0:s, :] = sp.NaN
temp[-s:, :, :] = sp.NaN
temp[:, -s:, :] = sp.NaN
temp[s : s + nl, s:nc, :] = im
## Apply the max filter
for i in range(s, nl + s): # Shift the origin to remove border effect
for j in range(s, nc + s):
for k in range(d):
out[i - s, j - s, k] = sp.nanmax(temp[i - s : i + 1 + s, j - s : j + s + 1, k])
return out.astype(im.dtype.name)
def doGstepSmooth(j):
dat, edat = data_struct.dat, data_struct.edat
Gsold, Gs, As = data_struct.Gsold, data_struct.Gs, data_struct.As
npix = data_struct.npix
ncomp = data_struct.ncomp
eps = data_struct.eps
if j > 0 and j < (npix - 1):
mult = 2
else:
mult = 1
# Covj = np.matrix(np.diag(1. / edat[:, j] ** 2))
# Aj = As.T * Covj * As + mult * eps * np.identity(ncomp)
# del Covj
# rewrite of less performant code
Aj = As.T * np.matrix((1.0 / edat[:, j] ** 2)[:, None] * np.asarray(As), copy=False)
Aj[np.arange(ncomp), np.arange(ncomp)] = np.asarray(
Aj[np.arange(ncomp), np.arange(ncomp)] + (mult * eps) * np.ones(ncomp)
).flatten()
if j > 0 and j < (npix - 1):
Fj = As.T * np.matrix(dat[:, j] / (edat[:, j]) ** 2, copy=False).T + eps * (Gsold[j - 1, :] + Gsold[j + 1, :]).T
elif j == 0:
Fj = As.T * np.matrix(dat[:, j] / (edat[:, j]) ** 2, copy=False).T + eps * Gsold[1, :].T
elif j == npix - 1:
Fj = As.T * np.matrix(dat[:, j] / (edat[:, j]) ** 2, copy=False).T + eps * Gsold[npix - 2, :].T
Gj = scipy.linalg.solve(Aj, Fj, sym_pos=True)
newGj = Gj.flatten()
oldGj = Gsold[j, :]
delta = scipy.nanmax(np.abs((newGj - oldGj) / (np.abs(oldGj).max() + 1e-100)))
Gs[j, :] = newGj
return delta
def initTau(self, pa, pb, qa, qb, qE):
# Method to initialise the precision of the noise
# Inputs:
# pa (float): 'a' parameter of the prior distribution
# pb (float): 'b' parameter of the prior distribution
# qb (float): initialisation of the 'b' parameter of the variational distribution
# qE (float): initial expectation of the variational distribution
tau_list = [None] * self.M
for m in range(self.M):
if self.lik[m] == "poisson":
tmp = 0.25 + 0.17 * s.nanmax(self.data[m], axis=0)
tau_list[m] = Constant_Node(dim=((self.N, self.D[m])),
value=s.repeat(
tmp[None, :], self.N, 0))
elif self.lik[m] == "bernoulli":
# seeger
# tau_list[m] = Constant_Node(dim=((self.N,self.D[m])), value=0.25)
# Jaakkola
tau_list[m] = Tau_Jaakkola(dim=((self.N, self.D[m])), value=1.)
elif self.lik[m] == "binomial":
print("Not implemented")
# tmp = 0.25*s.amax(self.data["tot"][m],axis=0)
# tau_list[m] = Constant_Node(dim=(self.D[m],), value=tmp)
elif self.lik[m] == "gaussian":
tau_list[m] = Tau_Node(dim=(self.D[m], ),
pa=pa[m],
pb=pb[m],
qa=qa[m],
qb=qb[m],
qE=qE[m])
self.Tau = Multiview_Mixed_Node(self.M, *tau_list)
self.nodes["Tau"] = self.Tau
def maximum_out_degree_fraction(cover, weights=None):
'''
Out Degree Fraction (ODF) of a node in a cluster is the ratio between its number of external (boundary) edges
and its internal edges. Maximum ODF returns the maximum fraction for the cluster.
'''
odf = out_degree_fraction(cover, weights=weights)
return [ nanmax(ratios) for ratios in odf ]
def veldist_1d_vrconvolve(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_vrconvolve
PURPOSE:
make a plot showing the influence of the los velocity uncertainties
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-09-11 - Written - Bovy (NYU)
"""
convolves= [0.02,0.04,0.08]#0, 5, 10, 20 km/s
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
thissavefilename= os.path.join(saveDir,'convolve_')+'%.1f.sav' % 0.
print "Restoring los-velocity distribution at distance uncertainties %.1f" % 0.
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
vlosds.append(vlosd)
basesavefilename= os.path.join(saveDir,'vrconvolve_')
for distsig in convolves:
thissavefilename= os.path.join(saveDir,'convolve_')+'%.1f.sav' % 0.
print "Restoring los-velocity distribution at distance uncertainties %.1f" % 0.
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
#Create Gaussian
gauss= sc.exp(-0.5*vloss**2./distsig**2.)
#gauss= gauss/sc.sum(gauss)/(vloss[1]-vloss[0])
vlosd= signal.convolve(vlosd,gauss,mode='same')
vlosd= vlosd/sc.sum(vlosd)/(vloss[1]-vloss[0])
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[0],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.nanmax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.6',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
overplot=True,zorder=2,lw=2.)
kms= r' \mathrm{km\ s}^{-1}$'
plot.bovy_text(r'$\mathrm{line-of-sight\ velocity\ uncertainties}$',title=True)
plot.bovy_text(0.4,.65,r'$\sigma_v = 0\ '+kms+'\n'+r'$\sigma_v = 5\ '+kms+'\n'+r'$\sigma_v = 10\ '+kms+'\n'+r'$\sigma_v = 20\ '+kms)
plot.bovy_end_print(plotfilename)
def CalcStats(samples):
return dict(
zip(['mean', 'max', 'min', 'sdeviation'], [
float(sp.mean(samples)),
float(sp.nanmax(samples)),
float(sp.nanmin(samples)),
float(sp.std(samples))
]))
def defaultValueStoppingCriterion(nIter, currentVArray, newVArray, criterion=0.001): diff = newVArray - currentVArray pct = diff / currentVArray a = abs(pct) # when we allow zero utility, sometimes the pct will have NaNs maxdiff = scipy.nanmax(a) if (scipy.isnan(maxdiff)): assert(False) return ((maxdiff < criterion), maxdiff)
def to_discrete_dataset(self, dfactor=36, dataset=None):
#get maximum and minimum values
print "getting feature summary"
f_summary = self.get_feature_summary()
print "min/max val"
f_min_max = {}
for name in f_summary.keys():
#if(nan in f_summary[name]):
# print "f_summary[name]", fsummary[name]
#try:
# f_summary[name].remove(nan)
#except:
# print "not removed"
#print f_summary, name
#print list(f_summary[name])
min_val = nanmin(list(f_summary[name]))
max_val = nanmax(list(f_summary[name]))
#if(isnan(min_val) or isnan(max_val)):
# print "name:", name, min_val, max_val
#else:
# print "name:", name, min_val, max_val
# print "nan in summary:", nan in f_summary[name]
# print "set:", f_summary[name]
# print "dataset.py: error neither should be nan"
# exit(0)
f_min_max[name] = [min_val, max_val]
#print name, [min_val, max_val]
#raw_input()
# print "computed keys"
# for key in f_min_max.keys():
# if "3d" in key and "w_pick" in key:
# print key, f_min_max[key]
#convert other datset if applicable
if (dataset == None):
observations = self.observations
else:
observations = dataset
obs_discrete = []
print "converting observations"
for o in observations:
dobs = o.to_discrete_observation(f_min_max, dfactor=dfactor)
dobs.annotation = o.annotation
obs_discrete.append(dobs)
print "constructing dataset"
return DiscreteDataset(
obs_discrete,
dfactor,
f_min_max=f_min_max,
feature_extractor_cls=self.feature_extractor_cls)
def compare_arrays(a, b, inc_time=False):
diffAbs = (sp.absolute(sp.subtract(a, b)))
diffMax = sp.nanmax(diffAbs)
diffEps = (diffMax / sys.float_info.epsilon
) * 2 # we have to mult by two as python epsilon is not correct
if inc_time:
return sp.column_stack((a[:, 0], diffAbs)), diffMax, diffEps
else:
return diffAbs, diffMax, diffEps
def initTau(self, pa=1e-3, pb=1e-3, qa=1., qb=1., qE=None):
"""Method to initialise the precision of the noise
PARAMETERS
----------
pa: float
'a' parameter of the prior distribution
pb :float
'b' parameter of the prior distribution
qb: float
initialisation of the 'b' parameter of the variational distribution
qE: float
initial expectation of the variational distribution
"""
tau_list = [None] * self.M
for m in range(self.M):
# Poisson noise model for count data
if self.lik[m] == "poisson":
tmp = 0.25 + 0.17 * s.nanmax(self.data[m], axis=0)
tmp = s.repeat(tmp[None, :], self.N, axis=0)
tau_list[m] = Tau_Seeger(dim=(self.N, self.D[m]), value=tmp)
# Bernoulli noise model for binary data
elif self.lik[m] == "bernoulli":
# tau_list[m] = Constant_Node(dim=(self.D[m],), value=s.ones(self.D[m])*0.25)
# tau_list[m] = Tau_Jaakkola(dim=(self.D[m],), value=0.25)
tau_list[m] = Tau_Jaakkola(dim=((self.N, self.D[m])), value=1.)
elif self.lik[m] == "zero_inflated":
# contains parameters to initialise both normal and jaakola tau
tau_list[m] = Zero_Inflated_Tau_Jaakkola(dim=((self.G,
self.D[m])),
value=1.,
pa=pa,
pb=pb,
qa=qa,
qb=qb,
groups=self.groups_ix,
qE=qE)
# Gaussian noise model for continuous data
elif self.lik[m] == "gaussian":
tau_list[m] = TauD_Node(dim=(self.G, self.D[m]),
pa=pa,
pb=pb,
qa=qa,
qb=qb,
groups=self.groups_ix,
qE=qE)
self.nodes["Tau"] = Multiview_Mixed_Node(self.M, *tau_list)
def get_cb_ticks(values):
min_tick = sp.nanmin(values)
max_tick = sp.nanmax(values)
med_tick = min_tick + (max_tick - min_tick) / 2.0
if max_tick > 1.0:
min_tick = sp.ceil(min_tick)
max_tick = sp.floor(max_tick)
med_tick = sp.around(med_tick)
else:
min_tick = sp.ceil(min_tick * 100.0) / 100.0
max_tick = sp.floor(max_tick * 100.0) / 100.0
med_tick = sp.around(med_tick, 2)
return [min_tick, med_tick, max_tick]
def CenteredLagProduct(rawbeams,numtype=sp.complex128,pulse =sp.ones(14)):
""" This function will create a centered lag product for each range using the
raw IQ given to it. It will form each lag for each pulse and then integrate
all of the pulses.
Inputs:
rawbeams - This is a NpxNs complex numpy array where Ns is number of
samples per pulse and Npu is number of pulses
N - The number of lags that will be created, default is 14.
numtype - The type of numbers used to create the data. Default is sp.complex128
Output:
acf_cent - This is a NrxNl complex numpy array where Nr is number of
range gate and Nl is number of lags.
"""
N=len(pulse)
# It will be assumed the data will be pulses vs rangne
rawbeams = rawbeams.transpose()
(Nr,Np) = rawbeams.shape
# Make masks for each piece of data
arex = sp.arange(0,N/2.0,0.5);
arback = sp.array([-sp.int_(sp.floor(k)) for k in arex]);
arfor = sp.array([sp.int_(sp.ceil(k)) for k in arex]) ;
# figure out how much range space will be kept
ap = sp.nanmax(abs(arback));
ep = Nr- sp.nanmax(arfor);
rng_ar_all = sp.arange(ap,ep);
# wearr = (1./(N-sp.tile((arfor-arback)[:,sp.newaxis],(1,Np)))).astype(numtype)
#acf_cent = sp.zeros((ep-ap,N))*(1+1j)
acf_cent = sp.zeros((ep-ap,N),dtype=numtype)
for irng in sp.arange(len(rng_ar_all)):
rng_ar1 =sp.int_(rng_ar_all[irng]) + arback
rng_ar2 = sp.int_(rng_ar_all[irng]) + arfor
# get all of the acfs across pulses # sum along the pulses
acf_tmp = sp.conj(rawbeams[rng_ar1,:])*rawbeams[rng_ar2,:]#*wearr
acf_ave = sp.sum(acf_tmp,1)
acf_cent[irng,:] = acf_ave# might need to transpose this
return acf_cent
def processDebug1(self, rawImagePath):
logger.info("using standard Andor 0 process function")
rawArray = self.read(rawImagePath)
if not self.optionsDict["process?"]:
return rawArray
[atomsArray, lightArray] = self.fastKineticsCrop(rawArray, 2)
if self.optionsDict["darkSubtraction"]:
darkArray = self.loadDarkImage(self.darkImagePath)
rawArray -= darkArray
rawArray.clip(1)
logger.info("atomsArray = %s" % atomsArray)
logger.info("lightArray = %s" % lightArray)
logger.info("atomsArray/lightArray = %s" % (atomsArray / lightArray))
logger.info("min , max atoms = %s, %s" %
(scipy.nanmin(atomsArray), scipy.nanmax(atomsArray)))
logger.info("min , max light = %s, %s" %
(scipy.nanmin(lightArray), scipy.nanmax(lightArray)))
logger.info("min , max atoms/light = %s, %s" % (scipy.nanmin(
atomsArray / lightArray), scipy.nanmax(atomsArray / lightArray)))
corrected = atomsArray / lightArray
if self.optionsDict["rotate?"]:
rotated = self.rotate(corrected, self.optionsDict["rotationAngle"])
return rotated
def _statisticsButton_fired(self):
from scipy.stats import pearsonr
xs, ys = self.dataSets[self.selectedDataSet]
mean = scipy.mean(ys)
median = scipy.median(ys)
std = scipy.std(ys)
minimum = scipy.nanmin(ys)
maximum = scipy.nanmax(ys)
peakToPeak = maximum - minimum
pearsonCorrelation = pearsonr(xs, ys)
resultString = "mean=%G , median=%G stdev =%G\nmin=%G,max=%G, pk-pk=%G\nPearson Correlation=(%G,%G)\n(stdev/mean)=%G" % (
mean, median, std, minimum, maximum, peakToPeak,
pearsonCorrelation[0], pearsonCorrelation[1], std / mean)
self.statisticsString = resultString
def print_verbose_message(self, i):
"""Method to print training statistics if Verbose is TRUE"""
# Memory usage (does not work in Windows)
# print('Peak memory usage: %.2f MB' % (resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / infer_platform() ))
# Variance explained
r2 = s.asarray(
self.calculate_variance_explained(total=True)).mean(axis=0)
r2[r2 < 0] = 0.
print("- Variance explained: " + " ".join([
"View %s: %.2f%%" % (m, 100 * r2[m]) for m in range(self.dim["M"])
]))
# Sparsity levels of the weights
W = self.nodes["W"].getExpectation()
foo = [s.mean(s.absolute(W[m]) < 1e-3) for m in range(self.dim["M"])]
print("- Fraction of zero weights: " + " ".join([
"View %s: %.0f%%" % (m, 100 * foo[m]) for m in range(self.dim["M"])
]))
# Correlation between factors
Z = self.nodes["Z"].getExpectation()
Z += s.random.normal(s.zeros(Z.shape), 1e-10)
r = s.absolute(corr(Z.T, Z.T))
s.fill_diagonal(r, 0)
print("- Maximum correlation between factors: %.2f" % (s.nanmax(r)))
# Factor norm
bar = s.mean(s.square(Z), axis=0)
print("- Factor norms: " +
" ".join(["%.2f" % bar[k] for k in range(Z.shape[1])]))
# Tau
tau = self.nodes["Tau"].getExpectation()
print("- Tau per view (average): " + " ".join([
"View %s: %.2f" % (m, tau[m].mean()) for m in range(self.dim["M"])
]))
#Sigma:
if 'Sigma' in self.nodes.keys():
sigma = self.nodes["Sigma"]
if i >= sigma.start_opt and i % sigma.opt_freq == 0:
print('Sigma node has been optimised:\n- Lengthscales = %s \n- Scale = %s' % \
(np.array2string(sigma.get_ls(), precision=2, separator=", "), np.array2string(1-sigma.get_zeta(), precision=2, separator=", ")))
print("\n")
def getImageData(self, imageFile):
logger.debug("pulling image data")
# The xs and ys used for the image plot range need to be the
# edges of the cells.
self.imageFile = imageFile
self.xs = scipy.linspace(0.0, self.pixelsX - 1, self.pixelsX)
self.ys = scipy.linspace(0.0, self.pixelsY - 1, self.pixelsY)
if not os.path.exists(imageFile):
#if no file is define the image is flat 0s of camera size
logger.error("image file not found. filling with zeros")
self.zs = scipy.zeros((self.pixelsX, self.pixelsY))
print self.zs
self.minZ = 0.0
self.maxZ = 1.0
self.model_changed = True
else:
try:
self.rawImage = scipy.misc.imread(imageFile)
self.zs = (self.rawImage - self.offset) / self.scale
if self.ODCorrectionBool:
logger.info("Correcting for OD saturation")
self.zs = scipy.log(
(1.0 - scipy.exp(-self.ODSaturationValue)) /
(scipy.exp(-self.zs) -
scipy.exp(-self.ODSaturationValue)))
#we should account for the fact if ODSaturation value is wrong or there is noise we can get complex numbers!
self.zs[scipy.imag(self.zs) > 0] = scipy.nan
self.zs = self.zs.astype(float)
self.minZ = scipy.nanmin(self.zs)
self.maxZ = scipy.nanmax(self.zs)
self.model_changed = True
for fit in self.fitList:
fit.xs = self.xs
fit.ys = self.ys
fit.zs = self.zs
except Exception as e:
logger.error("error in setting data %s" % e.message)
logger.debug(
"Sometimes we get an error unsupported operand type(s) for -: 'instance' and 'float'. "
)
logger.debug(
"checking for what could cause this . Tell Tim if you see this error message!!!!"
)
logger.debug("type(self.rawImage) -> %s" %
type(self.rawImage))
logger.debug("type(self.offset) -> %s" % type(self.offset))
def doAstep(i):
# we use the fact that dat,edat aren't changed on the way,
# so they shouldn't be copied to a different thread
dat, edat = data_struct.dat, data_struct.edat
Gs, As = data_struct.Gs, data_struct.As
Fi = np.matrix(dat[i] / edat[i] ** 2, copy=False) * Gs
# Covi = np.matrix(np.diag(1. / edat[i] ** 2), copy=False)
# Gi = Gs.T * Covi * Gs
# del Covi
Gi = Gs.T * np.matrix((1.0 / edat[i] ** 2)[:, None] * np.asarray(Gs), copy=False)
Ai = scipy.linalg.solve(Gi, Fi.T, sym_pos=True)
newAi = Ai.flatten()
oldAi = As[i, :]
delta = scipy.nanmax(np.abs((newAi - oldAi) / (np.abs(oldAi).max() + 1e-100)))
As[i, :] = newAi
return delta
def metrique_pheno_derivative(ndvi=sp.empty):
"""
This method use the second derivative of the NDVI to identifie the dates of beginning of season, end of season and more.
Parameters:
----------
ndvi : correspond à l'evolution DU NDVI pour un pixel sur une année
"""
try:
ndviMin = ndvi.min() #valeur minimale
ndviMax = ndvi.max() #valeur maximale
#ndviMean=ndvi.mean() #valeur moyenne
indMin = int(sp.median(sp.where(ndvi == ndviMin))) #indice du minimum
indMax = int(sp.median(sp.where(ndvi == ndviMax))) #indice du max
d1 = sp.convolve(ndvi, [1, -1],
'same') #first derivative approximation
d2 = sp.convolve(ndvi, [1, -2, 1],
'same') #second derivative approximation
k1 = d1[:-1] * d1[1:] #to find inflection point
k2 = d2[:-1] * d2[1:] #to find inflection point
ind0d1 = (sp.where(k1[:indMax] < 0))[0][-1]
ind0d2 = (sp.where(k2[:indMax] < 0))[0][-1]
sos = ind0d2
sos = sp.nanmax([ind0d1, ind0d2])
ind0d22 = (sp.where(k2[indMax + 1:] < 0))[0][0]
eos = ind0d22 + indMax
los = eos - sos
out = [sos, eos + 1, los, indMin + 1, indMax + 1, ndviMin,
ndviMax] # +1 parceque l'indice commence à 0
except:
out = [-1, -1, -1, -1, -1, -1, -1]
return out
def doAstep(i):
# we use the fact that dat,edat aren't changed on the way,
# so they shouldn't be copied to a different thread
dat, edat = data_struct.dat, data_struct.edat
Gs, As = data_struct.Gs, data_struct.As
Fi = np.matrix(dat[i] / edat[i]**2, copy=False) * Gs
# Covi = np.matrix(np.diag(1. / edat[i] ** 2), copy=False)
# Gi = Gs.T * Covi * Gs
# del Covi
Gi = Gs.T * np.matrix(
(1. / edat[i]**2)[:, None] * np.asarray(Gs), copy=False)
Ai = scipy.linalg.solve(Gi, Fi.T, sym_pos=True)
newAi = Ai.flatten()
oldAi = As[i, :]
delta = scipy.nanmax(
np.abs((newAi - oldAi) / (np.abs(oldAi).max() + 1e-100)))
As[i, :] = newAi
return delta
def doGstep(j):
dat, edat = data_struct.dat, data_struct.edat
Gs, As = data_struct.Gs, data_struct.As
# Covj = np.matrix(np.diag(1. / edat[:, j] ** 2), copy=False)
# Aj = As.T * Covj * As
# del Covj
# the rewrite uses the fact that
# diagonal matrix times matrix can be rewritten as
# np.matrix(xs[:,None]*np.asarray(Gs)) ==
# np.matrix(np.diag(xs))*Gs
Aj = As.T * np.matrix((1.0 / edat[:, j] ** 2)[:, None] * np.asarray(As), copy=False)
Fj = As.T * np.matrix((dat[:, j] / (edat[:, j]) ** 2), copy=False).T
Gj = scipy.linalg.solve(Aj, Fj, sym_pos=True)
newGj = Gj.flatten()
oldGj = Gs[j, :]
delta = scipy.nanmax(np.abs((newGj - oldGj) / (np.abs(oldGj).max() + 1e-100)))
Gs[j, :] = newGj
return delta
def get_ls_ls_market_price_dispersion(ls_ls_market_ids, master_price, series):
# if numpy.version.version = '1.8' or above => switch from scipy to numpy
# checks nb of prices (non nan) per period (must be 2 prices at least)
ls_ls_market_price_dispersion = []
for ls_market_ids in ls_ls_market_ids:
list_market_prices = [master_price[series][master_price['ids'].index(indiv_id)] for indiv_id in ls_market_ids]
arr_market_prices = np.array(list_market_prices, dtype = np.float32)
arr_nb_market_prices = (~np.isnan(arr_market_prices)).sum(0)
arr_bool_enough_market_prices = np.where(arr_nb_market_prices > 1, 1, np.nan)
arr_market_prices = arr_bool_enough_market_prices * arr_market_prices
range_price_array = scipy.nanmax(arr_market_prices, 0) - scipy.nanmin(arr_market_prices, axis = 0)
std_price_array = scipy.stats.nanstd(arr_market_prices, 0)
coeff_var_price_array = scipy.stats.nanstd(arr_market_prices, 0) / scipy.stats.nanmean(arr_market_prices, 0)
gain_from_search_array = scipy.stats.nanmean(arr_market_prices, 0) - scipy.nanmin(arr_market_prices, axis = 0)
list_list_market_price_dispersion.append(( ls_market_ids,
len(ls_market_ids),
range_price_array,
std_price_array,
coeff_var_price_array,
gain_from_search_array ))
return list_list_market_price_dispersion
def metrique_pheno_derivative(ndvi=sp.empty):
"""
This method use the second derivative of the NDVI to identifie the dates of beginning of season, end of season and more.
Parameters:
----------
ndvi : correspond à l'evolution DU NDVI pour un pixel sur une année
"""
try:
ndviMin=ndvi.min() #valeur minimale
ndviMax=ndvi.max() #valeur maximale
#ndviMean=ndvi.mean() #valeur moyenne
indMin=int(sp.median(sp.where(ndvi==ndviMin))) #indice du minimum
indMax=int(sp.median(sp.where(ndvi==ndviMax))) #indice du max
d1=sp.convolve(ndvi, [1, -1],'same') #first derivative approximation
d2=sp.convolve(ndvi, [1, -2, 1],'same') #second derivative approximation
k1=d1[:-1]*d1[1:] #to find inflection point
k2=d2[:-1]*d2[1:] #to find inflection point
ind0d1=(sp.where(k1[:indMax]<0))[0][-1]
ind0d2=(sp.where(k2[:indMax]<0))[0][-1]
sos=ind0d2
sos=sp.nanmax([ind0d1,ind0d2])
ind0d22=(sp.where(k2[indMax+1:]<0))[0][0]
eos=ind0d22+indMax
los=eos-sos
out=[sos,eos+1,los,indMin+1,indMax+1,ndviMin,ndviMax] # +1 parceque l'indice commence à 0
except:
out=[-1,-1,-1,-1,-1,-1,-1]
return out
def doGstepSmooth(j):
dat, edat = data_struct.dat, data_struct.edat
Gsold, Gs, As = data_struct.Gsold, data_struct.Gs, data_struct.As
npix = data_struct.npix
ncomp = data_struct.ncomp
eps = data_struct.eps
if j > 0 and j < (npix - 1):
mult = 2
else:
mult = 1
# Covj = np.matrix(np.diag(1. / edat[:, j] ** 2))
# Aj = As.T * Covj * As + mult * eps * np.identity(ncomp)
# del Covj
# rewrite of less performant code
Aj = As.T * np.matrix(
(1. / edat[:, j]**2)[:, None] * np.asarray(As), copy=False)
Aj[np.arange(ncomp),
np.arange(ncomp)] = np.asarray(Aj[np.arange(ncomp),
np.arange(ncomp)] +
(mult * eps) * np.ones(ncomp)).flatten()
if j > 0 and j < (npix - 1):
Fj = As.T * np.matrix(dat[:,j] / (edat[:,j]) ** 2, copy=False).T + \
eps * (Gsold[j - 1, :] + Gsold[j + 1, :]).T
elif j == 0:
Fj = As.T * np.matrix(dat[:,j] / (edat[:,j]) ** 2, copy=False).T + \
eps * Gsold[1, :].T
elif j == npix - 1:
Fj = As.T * np.matrix(dat[:,j] / (edat[:,j]) ** 2, copy=False).T + \
eps * Gsold[npix - 2, :].T
Gj = scipy.linalg.solve(Aj, Fj, sym_pos=True)
newGj = Gj.flatten()
oldGj = Gsold[j, :]
delta = scipy.nanmax(
np.abs((newGj - oldGj) / (np.abs(oldGj).max() + 1e-100)))
Gs[j, :] = newGj
return delta
def doGstep(j):
dat, edat = data_struct.dat, data_struct.edat
Gs, As = data_struct.Gs, data_struct.As
# Covj = np.matrix(np.diag(1. / edat[:, j] ** 2), copy=False)
# Aj = As.T * Covj * As
# del Covj
# the rewrite uses the fact that
# diagonal matrix times matrix can be rewritten as
# np.matrix(xs[:,None]*np.asarray(Gs)) ==
# np.matrix(np.diag(xs))*Gs
Aj = As.T * np.matrix(
(1. / edat[:, j]**2)[:, None] * np.asarray(As), copy=False)
Fj = As.T * np.matrix((dat[:, j] / (edat[:, j])**2), copy=False).T
Gj = scipy.linalg.solve(Aj, Fj, sym_pos=True)
newGj = Gj.flatten()
oldGj = Gs[j, :]
delta = scipy.nanmax(
np.abs((newGj - oldGj) / (np.abs(oldGj).max() + 1e-100)))
Gs[j, :] = newGj
return delta
def plotbeamparametersv2(times, configfile, maindir, fitdir='Fitted', params=['Ne'],
filetemplate='params', suptitle='Parameter Comparison',
werrors=False, nelog=True):
"""
This function will plot the desired parameters for each beam along range.
The values of the input and measured parameters will be plotted
Inputs
Times - A list of times that will be plotted.
configfile - The INI file with the simulation parameters that will be useds.
maindir - The directory the images will be saved in.
params - List of Parameter names that will be ploted. These need to match
in the ionocontainer names.
filetemplate - The first part of a the file names.
suptitle - The supertitle for the plots.
werrors - A bools that determines if the errors will be plotted.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
# rc('text', usetex=True)
maindir = Path(maindir)
ffit = maindir/fitdir/'fitteddata.h5'
inputfiledir = maindir/'Origparams'
(sensdict, simparams) = readconfigfile(configfile)
paramslower = [ip.lower() for ip in params]
Nt = len(times)
Np = len(params)
#Read in fitted data
Ionofit = IonoContainer.readh5(str(ffit))
dataloc = Ionofit.Sphere_Coords
pnames = Ionofit.Param_Names
pnameslower = sp.array([ip.lower() for ip in pnames.flatten()])
p2fit = [sp.argwhere(ip == pnameslower)[0][0]
if ip in pnameslower else None for ip in paramslower]
time2fit = [None]*Nt
# Have to fix this because of time offsets
if times[0] == 0:
times += Ionofit.Time_Vector[0, 0]
for itn, itime in enumerate(times):
filear = sp.argwhere(Ionofit.Time_Vector[:, 0] >= itime)
if len(filear) == 0:
filenum = len(Ionofit.Time_Vector)-1
else:
filenum = sp.argmin(sp.absolute(Ionofit.Time_Vector[:, 0]-itime))
time2fit[itn] = filenum
times_int = [Ionofit.Time_Vector[i] for i in time2fit]
# determine the beams
angles = dataloc[:, 1:]
rng = sp.unique(dataloc[:, 0])
b_arr = np.ascontiguousarray(angles).view(np.dtype((np.void,
angles.dtype.itemsize * angles.shape[1])))
_, idx, invidx = np.unique(b_arr, return_index=True, return_inverse=True)
beamlist = angles[idx]
Nb = beamlist.shape[0]
# Determine which imput files are to be used.
dirlist = sorted(inputfiledir.glob('*.h5'))
dirliststr = [str(i) for i in dirlist]
sortlist, outime, outfilelist,timebeg,timelist_s = IonoContainer.gettimes(dirliststr)
timelist = timebeg.copy()
time2file = [None]*Nt
time2intime = [None]*Nt
# go through times find files and then times in files
for itn, itime in enumerate(times):
filear = sp.argwhere(timelist >= itime)
if len(filear) == 0:
filenum = [len(timelist)-1]
else:
filenum = filear[0]
flist1 = []
timeinflist = []
for ifile in filenum:
filetimes = timelist_s[ifile]
log1 = (filetimes[:, 0] >= times_int[itn][0]) & (filetimes[:, 0] < times_int[itn][1])
log2 = (filetimes[:, 1] > times_int[itn][0]) & (filetimes[:, 1] <= times_int[itn][1])
log3 = (filetimes[:, 0] <= times_int[itn][0]) & (filetimes[:, 1] > times_int[itn][1])
log4 = (filetimes[:, 0] > times_int[itn][0]) & (filetimes[:, 1] < times_int[itn][1])
curtimes1 = sp.where(log1|log2|log3|log4)[0].tolist()
flist1 = flist1+ [ifile]*len(curtimes1)
timeinflist = timeinflist+curtimes1
time2intime[itn] = timeinflist
time2file[itn] = flist1
nfig = int(sp.ceil(Nt*Nb))
imcount = 0
curfilenum = -1
# Loop for the figures
for i_fig in range(nfig):
lines = [None]*2
labels = [None]*2
(figmplf, axmat) = plt.subplots(int(sp.ceil(Np/2)), 2, figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
# loop that goes through each axis loops through each parameter, beam
# then time.
for ax in axvec:
if imcount >= Nt*Nb*Np:
break
imcount_f = float(imcount)
itime = int(sp.floor(imcount_f/Nb/Np))
iparam = int(imcount_f/Nb-Np*itime)
ibeam = int(imcount_f-(itime*Np*Nb+iparam*Nb))
curbeam = beamlist[ibeam]
altlist = sp.sin(curbeam[1]*sp.pi/180.)*rng
curparm = paramslower[iparam]
# Use Ne from input to compare the ne derived from the power.
if curparm == 'nepow':
curparm_in = 'ne'
else:
curparm_in = curparm
curcoord = sp.zeros(3)
curcoord[1:] = curbeam
for iplot, filenum in enumerate(time2file[itime]):
if curfilenum != filenum:
curfilenum = filenum
datafilename = dirlist[filenum]
Ionoin = IonoContainer.readh5(str(datafilename))
if ('ti' in paramslower) or ('vi' in paramslower):
Ionoin = maketi(Ionoin)
pnames = Ionoin.Param_Names
pnameslowerin = sp.array([ip.lower() for ip in pnames.flatten()])
prmloc = sp.argwhere(curparm_in == pnameslowerin)
if prmloc.size != 0:
curprm = prmloc[0][0]
# build up parameter vector bs the range values by finding the closest point in space in the input
curdata = sp.zeros(len(rng))
for irngn, irng in enumerate(rng):
curcoord[0] = irng
tempin = Ionoin.getclosestsphere(curcoord)[0][time2intime[itime]]
Ntloc = tempin.shape[0]
tempin = sp.reshape(tempin, (Ntloc, len(pnameslowerin)))
curdata[irngn] = tempin[0, curprm]
#actual plotting of the input data
lines[0] = ax.plot(curdata, altlist, marker='o', c='b', linewidth=2)[0]
labels[0] = 'Input Parameters'
# Plot fitted data for the axis
indxkep = np.argwhere(invidx == ibeam)[:, 0]
curfit = Ionofit.Param_List[indxkep, time2fit[itime], p2fit[iparam]]
rng_fit = dataloc[indxkep, 0]
alt_fit = rng_fit*sp.sin(curbeam[1]*sp.pi/180.)
errorexist = 'n'+paramslower[iparam] in pnameslower
if errorexist and werrors:
eparam = sp.argwhere('n'+paramslower[iparam] == pnameslower)[0][0]
curerror = Ionofit.Param_List[indxkep, time2fit[itime], eparam]
lines[1] = ax.errorbar(curfit, alt_fit, xerr=curerror, fmt='-.',
c='g', linewidth=2)[0]
else:
lines[1] = ax.plot(curfit, alt_fit, marker='o', c='g', linewidth=2)[0]
labels[1] = 'Fitted Parameters'
# get and plot the input data
numplots = len(time2file[itime])
# set the limit for the parameter
if curparm == 'vi':
ax.set(xlim=[-1.25*sp.nanmax(sp.absolute(curfit)), 1.25*sp.nanmax(sp.absolute(curfit))])
elif curparm_in != 'ne':
ax.set(xlim=[0.75*sp.nanmin(curfit), sp.minimum(1.25*sp.nanmax(curfit), 8000.)])
elif (curparm_in == 'ne') and nelog:
ax.set_xscale('log')
ax.set_xlabel(params[iparam])
ax.set_ylabel('Alt km')
ax.set_title('{0} vs Altitude, Time: {1}s Az: {2}$^o$ El: {3}$^o$'.format(params[iparam], times[itime], *curbeam))
imcount += 1
# save figure
figmplf.suptitle(suptitle, fontsize=20)
if None in labels:
labels.remove(None)
lines.remove(None)
plt.figlegend(lines, labels, loc = 'lower center', ncol=5, labelspacing=0.)
fname = filetemplate+'_{0:0>3}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
def defaultPolicyStoppingCriterion(nIter, currentPolicyArrayList, greedyPolicyList, criterion=0.0001): diffList = [(greedyPolicyList[i] - currentPolicyArrayList[i]) for i in range(len(greedyPolicyList))] pctDiffList = [(diffList[i] / currentPolicyArrayList[i]) for i in range(len(greedyPolicyList))] maxdiffList = [scipy.nanmax(abs(diff)) for diff in diffList] maxdiff = scipy.amax(maxdiffList) return ((maxdiff < criterion), maxdiff)
def _sort_chunk(self):
"""sort this chunk on the calculated discriminant functions
method: "och"
Examples for overlap samples
tau=-2 tau=-1 tau=0 tau=1 tau=2
f1: |-----| |-----| |-----| |-----| |-----|
f2: |-----| |-----| |-----| |-----| |-----|
res: +++ ++++ +++++ ++++ +++
method: "sic"
TODO:
"""
# init
if self.nf == 0:
return
spk_ep = epochs_from_binvec(
sp.nanmax(self._disc, axis=1) > self._lpr_n)
if spk_ep.size == 0:
return
l, r = get_cut(self._tf)
for i in xrange(spk_ep.shape[0]):
# FIX: for now we just continue for empty epochs,
# where do they come from anyways?!
if spk_ep[i, 1] - spk_ep[i, 0] < 1:
continue
mc = self._disc[spk_ep[i, 0]:spk_ep[i, 1], :].argmax(0).argmax()
s = self._disc[spk_ep[i, 0]:spk_ep[i, 1], mc].argmax() + spk_ep[
i, 0]
spk_ep[i] = [s - l, s + r]
# check epochs
spk_ep = merge_epochs(spk_ep)
n_ep = spk_ep.shape[0]
for i in xrange(n_ep):
#
# method: overlap channels
#
if self._ovlp_taus is not None:
# get event time and channel
ep_t, ep_c = matrix_argmax(
self._disc[spk_ep[i, 0]:spk_ep[i, 1]])
ep_t += spk_ep[i, 0]
# lets fill in the results
if ep_c < self.nf:
# was single unit
fid = self.get_idx_for(ep_c)
self.rval[fid].append(ep_t + self._chunk_offset)
else:
# was overlap
my_oc_idx = self._oc_idx[ep_c]
fid0 = self.get_idx_for(my_oc_idx[0])
self.rval[fid0].append(ep_t + self._chunk_offset)
fid1 = self.get_idx_for(my_oc_idx[1])
self.rval[fid1].append(
ep_t + my_oc_idx[2] + self._chunk_offset)
#
# method: subtractive interference cancellation
#
else:
ep_fout = self._fout[spk_ep[i, 0]:spk_ep[i, 1], :]
ep_fout_norm = sp_la.norm(ep_fout)
ep_disc = self._disc[spk_ep[i, 0]:spk_ep[i, 1], :].copy()
niter = 0
while sp.nanmax(ep_disc) > self._lpr_n:
# warn on spike overflow
niter += 1
if niter > self.nf:
warnings.warn(
'more spikes than filters found! '
'epoch: [%d:%d] %d' % (
spk_ep[i][0] + self._chunk_offset,
spk_ep[i][1] + self._chunk_offset,
niter))
if niter > 2 * self.nf:
break
# find epoch details
ep_t = sp.nanargmax(sp.nanmax(ep_disc, axis=1))
ep_c = sp.nanargmax(ep_disc[ep_t])
# build subtrahend
sub = shifted_matrix_sub(
sp.zeros_like(ep_disc),
self._xcorrs[ep_c, :, :].T,
ep_t - self._tf + 1)
# apply subtrahend
if ep_fout_norm > sp_la.norm(ep_fout + sub):
## DEBUG
if self.verbose.get_has_plot(1):
try:
from spikeplot import xvf_tensor, plt, COLOURS
x_range = sp.arange(
spk_ep[i, 0] + self._chunk_offset,
spk_ep[i, 1] + self._chunk_offset)
f = plt.figure()
f.suptitle('spike epoch [%d:%d] #%d' %
(spk_ep[i, 0] + self._chunk_offset,
spk_ep[i, 1] + self._chunk_offset,
niter))
ax1 = f.add_subplot(211)
ax1.set_color_cycle(
['k'] + COLOURS[:self.nf] * 2)
ax1.plot(x_range, sp.zeros_like(x_range),
ls='--')
ax1.plot(x_range, ep_disc, label='pre_sub')
ax1.axvline(x_range[ep_t], c='k')
ax2 = f.add_subplot(212, sharex=ax1, sharey=ax1)
ax2.set_color_cycle(['k'] + COLOURS[:self.nf])
ax2.plot(x_range, sp.zeros_like(x_range),
ls='--')
ax2.plot(x_range, sub)
ax2.axvline(x_range[ep_t], c='k')
except:
pass
## BUGED
ep_disc += sub + self._lpr_s
if self._pr_s_b is not None:
bias, extend = self._pr_s_b
ep_disc[ep_t:min(ep_t + extend,
ep_disc.shape[0]), ep_c] -= bias
## DEBUG
if self.verbose.get_has_plot(1):
try:
ax1.plot(x_range, ep_disc, ls=':', lw=2,
label='post_sub')
ax1.legend(loc=2)
except:
pass
## BUGED
fid = self.get_idx_for(ep_c)
self.rval[fid].append(
spk_ep[i, 0] + ep_t + self._chunk_offset)
else:
break
del ep_fout, ep_disc, sub
def contourGD(geod,axstr,slicenum,vbounds=None,time = 0,gkey = None,cmap=defmap,
fig=None,ax=None,title='',cbar=True,m=None,levels=None):
""" """
poscoords = ['cartesian','wgs84','enu','ecef']
assert geod.coordnames.lower() in poscoords
if geod.coordnames.lower() in ['cartesian','enu','ecef']:
axdict = {'x':0,'y':1,'z':2}
veckeys = ['x','y','z']
elif geod.coordnames.lower() == 'wgs84':
axdict = {'lat':0,'long':1,'alt':2}# shows which row is this coordinate
veckeys = ['long','lat','alt']# shows which is the x, y and z axes for plotting
if type(axstr)==str:
axis=axstr
else:
axis= veckeys[axstr]
veckeys.remove(axis.lower())
veckeys.append(axis.lower())
datacoords = geod.dataloc
xyzvecs = {l:sp.unique(datacoords[:,axdict[l]]) for l in veckeys}
#make matrices
M1,M2 = sp.meshgrid(xyzvecs[veckeys[0]],xyzvecs[veckeys[1]])
slicevec = sp.unique(datacoords[:,axdict[axis]])
min_idx = sp.argmin(sp.absolute(slicevec-slicenum))
slicenum=slicevec[min_idx]
rec_coords = {axdict[veckeys[0]]:M1.flatten(),axdict[veckeys[1]]:M2.flatten(),
axdict[axis]:slicenum*sp.ones(M2.size)}
new_coords = sp.zeros((M1.size,3))
#make coordinates
for ckey in rec_coords.keys():
new_coords[:,ckey] = rec_coords[ckey]
#determine the data name
if gkey is None:
gkey = geod.data.keys[0]
# get the data location, first check if the data can be just reshaped then do a
# search
sliceindx = slicenum==datacoords[:,axdict[axis]]
datacoordred = datacoords[sliceindx]
rstypes = ['C','F','A']
nfounds = True
M1dlfl = datacoordred[:,axdict[veckeys[0]]]
M2dlfl = datacoordred[:,axdict[veckeys[1]]]
for ir in rstypes:
M1dl = sp.reshape(M1dlfl,M1.shape,order =ir)
M2dl = sp.reshape(M2dlfl,M1.shape,order =ir)
if sp.logical_and(sp.allclose(M1dl,M1),sp.allclose(M2dl,M2)):
nfounds=False
break
if nfounds:
dataout = geod.datareducelocation(new_coords,geod.coordnames,gkey)[:,time]
dataout = sp.reshape(dataout,M1.shape)
else:
dataout = sp.reshape(geod.data[gkey][sliceindx,time],M1.shape,order=ir)
title = insertinfo(title,gkey,geod.times[time,0],geod.times[time,1])
if (ax is None) and (fig is None):
fig = plt.figure(facecolor='white')
ax = fig.gca()
elif ax is None:
ax = fig.gca()
if vbounds is None:
vbounds=[sp.nanmin(dataout),sp.nanmax(dataout)]
if levels is None:
levels=sp.linspace(vbounds[0],vbounds[1],5)
if m is None:
ploth = ax.contour(M1,M2,dataout,levels = levels,vmin=vbounds[0], vmax=vbounds[1],cmap = cmap)
ax.axis([xyzvecs[veckeys[0]].min(), xyzvecs[veckeys[0]].max(),
xyzvecs[veckeys[1]].min(), xyzvecs[veckeys[1]].max()])
if cbar:
cbar2 = plt.colorbar(ploth, ax=ax, format='%.0e')
else:
cbar2 = None
ax.set_title(title)
ax.set_xlabel(veckeys[0])
ax.set_ylabel(veckeys[1])
else:
N1,N2 = m(M1,M2)
ploth = ax.contour(N1,N2,dataout,levels = levels,vmin=vbounds[0], vmax=vbounds[1],cmap = cmap)
if cbar:
#cbar2 = m.colorbar(ploth, format='%.0e')
cbar2 = m.colorbar(ploth)
else:
cbar2 = None
return(ploth,cbar2)
tmp_psi_ = hdf5_handles[t]['psi'][:, tmp_idx]
tmp_iso1 = hdf5_handles[t]['iso1'][:,
tmp_idx] * sf[t][:,
sp.newaxis]
tmp_iso2 = hdf5_handles[t]['iso2'][:,
tmp_idx] * sf[t][:,
sp.newaxis]
for mr in mr_t:
for i, p in enumerate(tissues):
tmp_psi = tmp_psi_[t_idx[(t, p)], :]
n_idx = sp.c_[tmp_iso1[t_idx[(t, p)], :].max(axis=0),
tmp_iso2[t_idx[(t, p)], :].max(
axis=0)].min(axis=1) < mr
tmp_psi[:, n_idx] = sp.nan
idx_nn = ~sp.isnan(tmp_psi)
d_psi = sp.nanmax(tmp_psi, axis=0) - sp.nanmin(tmp_psi,
axis=0)
d_psi[sp.isnan(d_psi)] = 0
for dp in d_psi_t:
if cc == 0:
count[(t, p)][(mr, dp)] = tmp_idx[
(sp.sum(idx_nn, axis=0) >= nan_t)
& (d_psi >= dp)]
else:
count[(t, p)][(mr, dp)] = sp.r_[
count[(t, p)][(mr, dp)],
tmp_idx[(sp.sum(idx_nn, axis=0) >= nan_t)
& (d_psi >= dp)]]
### store count as pre-processed pickle
cPickle.dump((count, tissues, dsets, tids, is_tumor, t_idx),
def veldist_1d_slope(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_slope
PURPOSE:
make a plot showing the influence of the shape of the rotation curve
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-05-15 - Written - Bovy (NYU)
"""
slopes= [-0.2,-0.1,0.,0.1,0.2]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'slope_')
for slope in slopes:
thissavefilename= basesavefilename+'%.1f.sav' % slope
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at slope %.1f" % slope
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at slope %.1f" % slope
potparams= (0.9,0.01,25.*_degtorad,.8,None)
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=slope,
potparams=potparams)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[2],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.nanmax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[0],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.60',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
overplot=True,zorder=2,lw=1.5)
plot.bovy_plot(vloss,vlosds[4],ls='-',color='0.3',
overplot=True,zorder=2,lw=1.5)
plot.bovy_text(r'$\mathrm{shape\ of\ the\ rotation\ curve}$',title=True)
plot.bovy_text(0.5,.5,r'$\beta = -0.2$'+'\n'+r'$\beta = -0.1$'+ '\n'+
r'$\beta = \phantom{-}0.0$'+'\n'+
r'$\beta= \phantom{-}0.1$'+'\n'+
r'$\beta= \phantom{-}0.2$')
plot.bovy_end_print(plotfilename)
def bovy_dens2d(X,**kwargs):
"""
NAME:
bovy_dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
colorbar - if True, add colorbar
shrink= colorbar argument: shrink the colorbar by the factor (optional)
Contours:
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels
are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
onedhists - if True, make one-d histograms on the sides
onedhistcolor - histogram color
retAxes= return all Axes instances
OUTPUT:
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
if kwargs.has_key('overplot'):
overplot= kwargs['overplot']
kwargs.pop('overplot')
else:
overplot= False
if not overplot:
pyplot.figure()
if kwargs.has_key('xlabel'):
xlabel= kwargs['xlabel']
kwargs.pop('xlabel')
else:
xlabel=None
if kwargs.has_key('ylabel'):
ylabel= kwargs['ylabel']
kwargs.pop('ylabel')
else:
ylabel=None
if kwargs.has_key('zlabel'):
zlabel= kwargs['zlabel']
kwargs.pop('zlabel')
else:
zlabel=None
if kwargs.has_key('extent'):
extent= kwargs['extent']
kwargs.pop('extent')
else:
if kwargs.has_key('xrange'):
xlimits=list(kwargs['xrange'])
kwargs.pop('xrange')
else:
xlimits=[0,X.shape[0]]
if kwargs.has_key('yrange'):
ylimits=list(kwargs['yrange'])
kwargs.pop('yrange')
else:
ylimits=[0,X.shape[1]]
extent= xlimits+ylimits
if not kwargs.has_key('aspect'):
kwargs['aspect']= (xlimits[1]-xlimits[0])/float(ylimits[1]-ylimits[0])
if kwargs.has_key('noaxes'):
noaxes= kwargs['noaxes']
kwargs.pop('noaxes')
else:
noaxes= False
if (kwargs.has_key('contours') and kwargs['contours']) or \
kwargs.has_key('levels') or \
(kwargs.has_key('cntrmass') and kwargs['cntrmass']):
contours= True
else:
contours= False
if kwargs.has_key('contours'): kwargs.pop('contours')
if kwargs.has_key('levels'):
levels= kwargs['levels']
kwargs.pop('levels')
elif contours:
if kwargs.has_key('cntrmass') and kwargs['cntrmass']:
levels= sc.linspace(0.,1.,_DEFAULTNCNTR)
elif True in sc.isnan(sc.array(X)):
levels= sc.linspace(sc.nanmin(X),sc.nanmax(X),_DEFAULTNCNTR)
else:
levels= sc.linspace(sc.amin(X),sc.amax(X),_DEFAULTNCNTR)
if kwargs.has_key('cntrmass') and kwargs['cntrmass']:
cntrmass= True
kwargs.pop('cntrmass')
else:
cntrmass= False
if kwargs.has_key('cntrmass'): kwargs.pop('cntrmass')
if kwargs.has_key('cntrcolors'):
cntrcolors= kwargs['cntrcolors']
kwargs.pop('cntrcolors')
elif contours:
cntrcolors='k'
if kwargs.has_key('cntrlabel') and kwargs['cntrlabel']:
cntrlabel= True
kwargs.pop('cntrlabel')
else:
cntrlabel= False
if kwargs.has_key('cntrlabel'): kwargs.pop('cntrlabel')
if kwargs.has_key('cntrlw'):
cntrlw= kwargs['cntrlw']
kwargs.pop('cntrlw')
elif contours:
cntrlw= None
if kwargs.has_key('cntrls'):
cntrls= kwargs['cntrls']
kwargs.pop('cntrls')
elif contours:
cntrls= None
if kwargs.has_key('cntrlabelsize'):
cntrlabelsize= kwargs['cntrlabelsize']
kwargs.pop('cntrlabelsize')
elif contours:
cntrlabelsize= None
if kwargs.has_key('cntrlabelcolors'):
cntrlabelcolors= kwargs['cntrlabelcolors']
kwargs.pop('cntrlabelcolors')
elif contours:
cntrlabelcolors= None
if kwargs.has_key('cntrinline'):
cntrinline= kwargs['cntrinline']
kwargs.pop('cntrinline')
elif contours:
cntrinline= None
if kwargs.has_key('retCumImage'):
retCumImage= kwargs['retCumImage']
kwargs.pop('retCumImage')
else:
retCumImage= False
if kwargs.has_key('colorbar'):
cb= kwargs['colorbar']
kwargs.pop('colorbar')
else:
cb= False
if kwargs.has_key('shrink'):
shrink= kwargs['shrink']
kwargs.pop('shrink')
else:
shrink= None
if kwargs.has_key('onedhists'):
onedhists= kwargs['onedhists']
kwargs.pop('onedhists')
else:
onedhists= False
if kwargs.has_key('onedhistcolor'):
onedhistcolor= kwargs['onedhistcolor']
kwargs.pop('onedhistcolor')
else:
onedhistcolor= 'k'
if kwargs.has_key('retAxes'):
retAxes= kwargs['retAxes']
kwargs.pop('retAxes')
else:
retAxes= False
if onedhists:
if overplot: fig= pyplot.gcf()
else: fig= pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
axHistx = pyplot.axes(rect_histx)
axHisty = pyplot.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
ax=pyplot.gca()
ax.set_autoscale_on(False)
out= pyplot.imshow(X,extent=extent,**kwargs)
pyplot.axis(extent)
_add_axislabels(xlabel,ylabel)
_add_ticks()
#Add colorbar
if cb:
if shrink is None:
if kwargs.has_key('aspect'):
shrink= sc.amin([float(kwargs['aspect'])*0.87,1.])
else:
shrink= 0.87
CB1= pyplot.colorbar(out,shrink=shrink)
if not zlabel is None:
if zlabel[0] != '$':
thiszlabel=r'$'+zlabel+'$'
else:
thiszlabel=zlabel
CB1.set_label(zlabel)
if contours or retCumImage:
if kwargs.has_key('aspect'):
aspect= kwargs['aspect']
else:
aspect= None
if kwargs.has_key('origin'):
origin= kwargs['origin']
else:
origin= None
if cntrmass:
#Sum from the top down!
X[sc.isnan(X)]= 0.
sortindx= sc.argsort(X.flatten())[::-1]
cumul= sc.cumsum(sc.sort(X.flatten())[::-1])/sc.sum(X.flatten())
cntrThis= sc.zeros(sc.prod(X.shape))
cntrThis[sortindx]= cumul
cntrThis= sc.reshape(cntrThis,X.shape)
else:
cntrThis= X
if contours:
cont= pyplot.contour(cntrThis,levels,colors=cntrcolors,
linewidths=cntrlw,extent=extent,aspect=aspect,
linestyles=cntrls,origin=origin)
if cntrlabel:
pyplot.clabel(cont,fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
#Add onedhists
if not onedhists:
if retCumImage:
return cntrThis
elif retAxes:
return pyplot.gca()
else:
return out
histx= sc.nansum(X.T,axis=1)*m.fabs(ylimits[1]-ylimits[0])/X.shape[1] #nansum bc nan is *no dens value*
histy= sc.nansum(X.T,axis=0)*m.fabs(xlimits[1]-xlimits[0])/X.shape[0]
histx[sc.isnan(histx)]= 0.
histy[sc.isnan(histy)]= 0.
dx= (extent[1]-extent[0])/float(len(histx))
axHistx.plot(sc.linspace(extent[0]+dx,extent[1]-dx,len(histx)),histx,
drawstyle='steps-mid',color=onedhistcolor)
dy= (extent[3]-extent[2])/float(len(histy))
axHisty.plot(histy,sc.linspace(extent[2]+dy,extent[3]-dy,len(histy)),
drawstyle='steps-mid',color=onedhistcolor)
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
axHistx.set_ylim( 0, 1.2*sc.amax(histx))
axHisty.set_xlim( 0, 1.2*sc.amax(histy))
if retCumImage:
return cntrThis
elif retAxes:
return (axScatter,axHistx,axHisty)
else:
return out
#cmd+="\cp -p "+northxyz_grd_path+" "+northxyz_filtered_grd_path+"\n";
#subprocess.call(cmd,shell=True);
out_f=netcdf.netcdf_file(eastxyz_filtered_grd_path,"w",True);
out_f.createDimension("x",x.shape[0]);
out_x=out_f.createVariable("x","f",("x",));
out_x[:]=x[:];
out_x._attributes["actual_range"]=scipy.array([x.min(), x.max()]);
out_f.createDimension("y",y.shape[0]);
out_y=out_f.createVariable("y","f",("y",));
out_y[:]=y[:];
out_y._attributes["actual_range"]=scipy.array([y.min(), y.max()]);
data_out=scipy.arange(x.shape[0]*y.shape[0]);
data_out.shape=(y.shape[0],x.shape[0]);
out_z=out_f.createVariable("z",scipy.dtype("float32").char,("y","x"));
out_z._attributes["actual_range"]=scipy.array([scipy.nanmin(eastvel), scipy.nanmax(eastvel)]);
out_z[:]=eastvel[:];
out_z._attributes["_FillValue"]="nan";
out_f.flush();
out_f.sync();
out_f.close();
exit();
# Write grid files...
cmd ="\nxyz2grd "+eastvel+" "+R+" -G"+eastxyz_filtered_grd_path+" -I120=\n";
cmd+="\nxyz2grd "+northvel+" "+R+" -G"+northxyz_filtered_grd_path+" -I120=\n";
def bovy_dens2d(X, **kwargs):
"""
NAME:
bovy_dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
Contours:
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels
are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
OUTPUT:
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
if 'overplot' in kwargs:
overplot = kwargs['overplot']
kwargs.pop('overplot')
else:
overplot = False
if not overplot:
pyplot.figure()
ax = pyplot.gca()
ax.set_autoscale_on(False)
if 'xlabel' in kwargs:
xlabel = kwargs['xlabel']
kwargs.pop('xlabel')
else:
xlabel = None
if 'ylabel' in kwargs:
ylabel = kwargs['ylabel']
kwargs.pop('ylabel')
else:
ylabel = None
if 'extent' in kwargs:
extent = kwargs['extent']
kwargs.pop('extent')
else:
if 'xrange' in kwargs:
xlimits = list(kwargs['xrange'])
kwargs.pop('xrange')
else:
xlimits = [0, X.shape[0]]
if 'yrange' in kwargs:
ylimits = list(kwargs['yrange'])
kwargs.pop('yrange')
else:
ylimits = [0, X.shape[1]]
extent = xlimits + ylimits
if 'noaxes' in kwargs:
noaxes = kwargs['noaxes']
kwargs.pop('noaxes')
else:
noaxes = False
if 'contours' in kwargs and kwargs['contours']:
contours = True
kwargs.pop('contours')
elif kwargs.has_key('levels') or 'cntrmass' in kwargs:
contours = True
else:
contours = False
if 'contours' in kwargs:
kwargs.pop('contours')
if 'levels' in kwargs:
levels = kwargs['levels']
kwargs.pop('levels')
elif contours:
if 'cntrmass' in kwargs and kwargs['cntrmass']:
levels = sc.linspace(0., 1., _DEFAULTNCNTR)
elif True in sc.isnan(sc.array(X)):
levels = sc.linspace(sc.nanmin(X), sc.nanmax(X), _DEFAULTNCNTR)
else:
levels = sc.linspace(sc.amin(X), sc.amax(X), _DEFAULTNCNTR)
if 'cntrmass' in kwargs and kwargs['cntrmass']:
cntrmass = True
kwargs.pop('cntrmass')
else:
cntrmass = False
if 'cntrmass' in kwargs: kwargs.pop('cntrmass')
if 'cntrcolors' in kwargs:
cntrcolors = kwargs['cntrcolors']
kwargs.pop('cntrcolors')
elif contours:
cntrcolors = 'k'
if 'cntrlabel' in kwargs and kwargs['cntrlabel']:
cntrlabel = True
kwargs.pop('cntrlabel')
else:
cntrlabel = False
if 'cntrlabel' in kwargs: kwargs.pop('cntrlabel')
if 'cntrlw' in kwargs:
cntrlw = kwargs['cntrlw']
kwargs.pop('cntrlw')
elif contours:
cntrlw = None
if 'cntrls' in kwargs:
cntrls = kwargs['cntrls']
kwargs.pop('cntrls')
elif contours:
cntrls = None
if 'cntrlabelsize' in kwargs:
cntrlabelsize = kwargs['cntrlabelsize']
kwargs.pop('cntrlabelsize')
elif contours:
cntrlabelsize = None
if 'cntrlabelcolors' in kwargs:
cntrlabelcolors = kwargs['cntrlabelcolors']
kwargs.pop('cntrlabelcolors')
elif contours:
cntrlabelcolors = None
if 'cntrinline' in kwargs:
cntrinline = kwargs['cntrinline']
kwargs.pop('cntrinline')
elif contours:
cntrinline = None
if 'retCumImage' in kwargs:
retCumImage = kwargs['retCumImage']
kwargs.pop('retCumImage')
else:
retCumImage = False
out = pyplot.imshow(X, extent=extent, **kwargs)
pyplot.axis(extent)
_add_axislabels(xlabel, ylabel)
_add_ticks()
if contours:
if 'aspect' in kwargs:
aspect = kwargs['aspect']
else:
aspect = None
if 'origin' in kwargs:
origin = kwargs['origin']
else:
origin = None
if cntrmass:
#Sum from the top down!
sortindx = sc.argsort(X.flatten())[::-1]
cumul = sc.cumsum(sc.sort(X.flatten())[::-1]) / sc.sum(X.flatten())
cntrThis = sc.zeros(sc.prod(X.shape))
cntrThis[sortindx] = cumul
cntrThis = sc.reshape(cntrThis, X.shape)
else:
cntrThis = X
cont = pyplot.contour(cntrThis,
levels,
colors=cntrcolors,
linewidths=cntrlw,
extent=extent,
aspect=aspect,
linestyles=cntrls,
origin=origin)
if cntrlabel:
pyplot.clabel(cont,
fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
if retCumImage:
return cntrThis
else:
return out
def veldist_1d_convolve(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_convolve
PURPOSE:
make a plot showing the influence of the distance uncertainties
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-05-15 - Written - Bovy (NYU)
"""
convolves= [0.,0.2,0.3]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'convolve_')
for distsig in convolves:
thissavefilename= basesavefilename+'%.1f.sav' % distsig
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at distance uncertainties %.1f" % distsig
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at distance uncertainties %.1f" % distsig
potparams= (0.9,0.01,25.*_degtorad,.8,None)
if distsig == 0.:
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams)
else:
vlosd= predictVlosConvolve(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams,
convolve=distsig)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[0],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.nanmax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.6',
overplot=True,zorder=2,lw=2.)
#plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
# overplot=True,zorder=2,lw=2.)
plot.bovy_text(r'$\mathrm{distance\ uncertainties}$',title=True)
plot.bovy_text(0.5,.65,r'$\sigma_d = 0$'+'\n'+r'$\sigma_d = 20 \%$'+'\n'+r'$\sigma_d = 30 \%$')
plot.bovy_end_print(plotfilename)
ax = Axes3D(fig)
#ax.plot3D(p[:,0], p[:,1], p[:,2]) #Plots the 3D graph - Stationary
def func(
k
): #########One of the input parameters needed for the animation.FuncAnimated which is used for iterating over the new points
step = 10 * k #The step size regulates the speed of the animation, very easy to change, just increase integer to speed up and decrease to slow down
ax.plot3D(p[:step, 0], p[:step, 1], p[:step, 2],
color='g') #For each i integrates the new function
###################################################################################
###Degfining the axes
ax.set_xlim3d([sp.nanmin(p[:, 0]), sp.nanmax(p[:, 0])
]) ########ABLE TO PLOT AXIS EXCLUDING NAN VALUES
ax.set_xlabel('X')
ax.set_ylim3d([sp.nanmin(p[:, 1]), sp.nanmax(p[:, 1])])
ax.set_ylabel('Y')
ax.set_zlim3d([sp.nanmin(p[:, 2]), sp.nanmax(p[:, 2])])
ax.set_zlabel('Z')
###################################################################################
# ax.set_xlim3d([sp.amin(p[:,0]),sp.amax(p[:,0])]) #Axes without considering the nan values
# ax.set_xlabel('X')
#
# ax.set_ylim3d([sp.amin(p[:,1]),sp.amax(p[:,1])])
# ax.set_ylabel('Y')
def contourGD(geod,axstr,slicenum,vbounds=None,time = 0,gkey = None,cmap=defmap,
fig=None,ax=None,title='',cbar=True,m=None,levels=None):
""" """
poscoords = ['cartesian','wgs84','enu','ecef']
assert geod.coordnames.lower() in poscoords
if geod.coordnames.lower() in ['cartesian','enu','ecef']:
axdict = {'x':0,'y':1,'z':2}
veckeys = ['x','y','z']
elif geod.coordnames.lower() == 'wgs84':
axdict = {'lat':0,'long':1,'alt':2}# shows which row is this coordinate
veckeys = ['long','lat','alt']# shows which is the x, y and z axes for plotting
if type(axstr)==str:
axis=axstr
else:
axis= veckeys[axstr]
veckeys.remove(axis.lower())
veckeys.append(axis.lower())
datacoords = geod.dataloc
xyzvecs = {l:sp.unique(datacoords[:,axdict[l]]) for l in veckeys}
#make matrices
M1,M2 = sp.meshgrid(xyzvecs[veckeys[0]],xyzvecs[veckeys[1]])
slicevec = sp.unique(datacoords[:,axdict[axis]])
min_idx = sp.argmin(sp.absolute(slicevec-slicenum))
slicenum=slicevec[min_idx]
rec_coords = {axdict[veckeys[0]]:M1.flatten(),axdict[veckeys[1]]:M2.flatten(),
axdict[axis]:slicenum*sp.ones(M2.size)}
new_coords = sp.zeros((M1.size,3))
#make coordinates
for ckey in rec_coords.keys():
new_coords[:,ckey] = rec_coords[ckey]
#determine the data name
if gkey is None:
gkey = geod.data.keys[0]
# get the data location, first check if the data can be just reshaped then do a
# search
sliceindx = slicenum==datacoords[:,axdict[axis]]
datacoordred = datacoords[sliceindx]
rstypes = ['C','F','A']
nfounds = True
M1dlfl = datacoordred[:,axdict[veckeys[0]]]
M2dlfl = datacoordred[:,axdict[veckeys[1]]]
for ir in rstypes:
M1dl = sp.reshape(M1dlfl,M1.shape,order =ir)
M2dl = sp.reshape(M2dlfl,M1.shape,order =ir)
if sp.logical_and(sp.allclose(M1dl,M1),sp.allclose(M2dl,M2)):
nfounds=False
break
if nfounds:
dataout = geod.datareducelocation(new_coords,geod.coordnames,gkey)[:,time]
dataout = sp.reshape(dataout,M1.shape)
else:
dataout = sp.reshape(geod.data[gkey][sliceindx,time],M1.shape,order=ir)
title = insertinfo(title,gkey,geod.times[time,0],geod.times[time,1])
if (ax is None) and (fig is None):
fig = plt.figure(facecolor='white')
ax = fig.gca()
elif ax is None:
ax = fig.gca()
if vbounds is None:
vbounds=[sp.nanmin(dataout),sp.nanmax(dataout)]
if levels is None:
levels=sp.linspace(vbounds[0],vbounds[1],5)
if m is None:
ploth = ax.contour(M1,M2,dataout,levels = levels,vmin=vbounds[0], vmax=vbounds[1],cmap = cmap)
ax.axis([xyzvecs[veckeys[0]].min(), xyzvecs[veckeys[0]].max(),
xyzvecs[veckeys[1]].min(), xyzvecs[veckeys[1]].max()])
if cbar:
cbar2 = plt.colorbar(ploth, ax=ax, format='%.0e')
else:
cbar2 = None
ax.set_title(title)
ax.set_xlabel(veckeys[0])
ax.set_ylabel(veckeys[1])
else:
N1,N2 = m(M1,M2)
ploth = ax.contour(N1,N2,dataout,levels = levels,vmin=vbounds[0], vmax=vbounds[1],cmap = cmap)
if cbar:
#cbar2 = m.colorbar(ploth, format='%.0e')
cbar2 = m.colorbar(ploth)
else:
cbar2 = None
return(ploth,cbar2)
def bovy_dens2d(X,**kwargs):
"""
NAME:
bovy_dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
colorbar - if True, add colorbar
shrink= colorbar argument: shrink the colorbar by the factor (optional)
conditional - normalize each column separately (for probability densities, i.e., cntrmass=True)
Contours:
justcontours - if True, only draw contours
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
cntrSmooth - use ndimage.gaussian_filter to smooth before contouring
onedhists - if True, make one-d histograms on the sides
onedhistcolor - histogram color
retAxes= return all Axes instances
retCont= return the contour instance
OUTPUT:
plot to output device, Axes instances depending on input
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
overplot= kwargs.pop('overplot',False)
if not overplot:
pyplot.figure()
xlabel= kwargs.pop('xlabel',None)
ylabel= kwargs.pop('ylabel',None)
zlabel= kwargs.pop('zlabel',None)
if 'extent' in kwargs:
extent= kwargs.pop('extent')
else:
xlimits= kwargs.pop('xrange',[0,X.shape[1]])
ylimits= kwargs.pop('yrange',[0,X.shape[0]])
extent= xlimits+ylimits
if not 'aspect' in kwargs:
kwargs['aspect']= (xlimits[1]-xlimits[0])/float(ylimits[1]-ylimits[0])
noaxes= kwargs.pop('noaxes',False)
justcontours= kwargs.pop('justcontours',False)
if ('contours' in kwargs and kwargs['contours']) or \
'levels' in kwargs or justcontours or \
('cntrmass' in kwargs and kwargs['cntrmass']):
contours= True
else:
contours= False
kwargs.pop('contours',None)
if 'levels' in kwargs:
levels= kwargs['levels']
kwargs.pop('levels')
elif contours:
if 'cntrmass' in kwargs and kwargs['cntrmass']:
levels= sc.linspace(0.,1.,_DEFAULTNCNTR)
elif True in sc.isnan(sc.array(X)):
levels= sc.linspace(sc.nanmin(X),sc.nanmax(X),_DEFAULTNCNTR)
else:
levels= sc.linspace(sc.amin(X),sc.amax(X),_DEFAULTNCNTR)
cntrmass= kwargs.pop('cntrmass',False)
conditional= kwargs.pop('conditional',False)
cntrcolors= kwargs.pop('cntrcolors','k')
cntrlabel= kwargs.pop('cntrlabel',False)
cntrlw= kwargs.pop('cntrlw',None)
cntrls= kwargs.pop('cntrls',None)
cntrSmooth= kwargs.pop('cntrSmooth',None)
cntrlabelsize= kwargs.pop('cntrlabelsize',None)
cntrlabelcolors= kwargs.pop('cntrlabelcolors',None)
cntrinline= kwargs.pop('cntrinline',None)
retCumImage= kwargs.pop('retCumImage',False)
cb= kwargs.pop('colorbar',False)
shrink= kwargs.pop('shrink',None)
onedhists= kwargs.pop('onedhists',False)
onedhistcolor= kwargs.pop('onedhistcolor','k')
retAxes= kwargs.pop('retAxes',False)
retCont= kwargs.pop('retCont',False)
if onedhists:
if overplot: fig= pyplot.gcf()
else: fig= pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
axHistx = pyplot.axes(rect_histx)
axHisty = pyplot.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
ax=pyplot.gca()
ax.set_autoscale_on(False)
if conditional:
plotthis= X/sc.tile(sc.sum(X,axis=0),(X.shape[1],1))
else:
plotthis= X
if not justcontours:
out= pyplot.imshow(plotthis,extent=extent,**kwargs)
if not overplot:
pyplot.axis(extent)
_add_axislabels(xlabel,ylabel)
_add_ticks()
#Add colorbar
if cb and not justcontours:
if shrink is None:
shrink= sc.amin([float(kwargs.pop('aspect',1.))*0.87,1.])
CB1= pyplot.colorbar(out,shrink=shrink)
if not zlabel is None:
if zlabel[0] != '$':
thiszlabel=r'$'+zlabel+'$'
else:
thiszlabel=zlabel
CB1.set_label(thiszlabel)
if contours or retCumImage:
aspect= kwargs.get('aspect',None)
origin= kwargs.get('origin',None)
if cntrmass:
#Sum from the top down!
plotthis[sc.isnan(plotthis)]= 0.
sortindx= sc.argsort(plotthis.flatten())[::-1]
cumul= sc.cumsum(sc.sort(plotthis.flatten())[::-1])/sc.sum(plotthis.flatten())
cntrThis= sc.zeros(sc.prod(plotthis.shape))
cntrThis[sortindx]= cumul
cntrThis= sc.reshape(cntrThis,plotthis.shape)
else:
cntrThis= plotthis
if contours:
if not cntrSmooth is None:
cntrThis= ndimage.gaussian_filter(cntrThis,cntrSmooth,
mode='nearest')
cont= pyplot.contour(cntrThis,levels,colors=cntrcolors,
linewidths=cntrlw,extent=extent,aspect=aspect,
linestyles=cntrls,origin=origin)
if cntrlabel:
pyplot.clabel(cont,fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
#Add onedhists
if not onedhists:
if retCumImage:
return cntrThis
elif retAxes:
return pyplot.gca()
elif retCont:
return cont
elif justcontours:
return cntrThis
else:
return out
histx= sc.nansum(X.T,axis=1)*m.fabs(ylimits[1]-ylimits[0])/X.shape[1] #nansum bc nan is *no dens value*
histy= sc.nansum(X.T,axis=0)*m.fabs(xlimits[1]-xlimits[0])/X.shape[0]
histx[sc.isnan(histx)]= 0.
histy[sc.isnan(histy)]= 0.
dx= (extent[1]-extent[0])/float(len(histx))
axHistx.plot(sc.linspace(extent[0]+dx,extent[1]-dx,len(histx)),histx,
drawstyle='steps-mid',color=onedhistcolor)
dy= (extent[3]-extent[2])/float(len(histy))
axHisty.plot(histy,sc.linspace(extent[2]+dy,extent[3]-dy,len(histy)),
drawstyle='steps-mid',color=onedhistcolor)
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
axHistx.set_ylim( 0, 1.2*sc.amax(histx))
axHisty.set_xlim( 0, 1.2*sc.amax(histy))
if retCumImage:
return cntrThis
elif retAxes:
return (axScatter,axHistx,axHisty)
elif justcontours:
return cntrThis
else:
return out
def bovy_dens2d(X,**kwargs):
"""
NAME:
bovy_dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
colorbar - if True, add colorbar
shrink= colorbar argument: shrink the colorbar by the factor (optional)
Contours:
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels
are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
OUTPUT:
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
if kwargs.has_key('overplot'):
overplot= kwargs['overplot']
kwargs.pop('overplot')
else:
overplot= False
if not overplot:
pyplot.figure()
ax=pyplot.gca()
ax.set_autoscale_on(False)
if kwargs.has_key('xlabel'):
xlabel= kwargs['xlabel']
kwargs.pop('xlabel')
else:
xlabel=None
if kwargs.has_key('ylabel'):
ylabel= kwargs['ylabel']
kwargs.pop('ylabel')
else:
ylabel=None
if kwargs.has_key('zlabel'):
zlabel= kwargs['zlabel']
kwargs.pop('zlabel')
else:
zlabel=None
if kwargs.has_key('extent'):
extent= kwargs['extent']
kwargs.pop('extent')
else:
if kwargs.has_key('xrange'):
xlimits=list(kwargs['xrange'])
kwargs.pop('xrange')
else:
xlimits=[0,X.shape[0]]
if kwargs.has_key('yrange'):
ylimits=list(kwargs['yrange'])
kwargs.pop('yrange')
else:
ylimits=[0,X.shape[1]]
extent= xlimits+ylimits
if not kwargs.has_key('aspect'):
kwargs['aspect']= (xlimits[1]-xlimits[0])/float(ylimits[1]-ylimits[0])
if kwargs.has_key('noaxes'):
noaxes= kwargs['noaxes']
kwargs.pop('noaxes')
else:
noaxes= False
if (kwargs.has_key('contours') and kwargs['contours']) or \
kwargs.has_key('levels') or \
(kwargs.has_key('cntrmass') and kwargs['cntrmass']):
contours= True
else:
contours= False
if kwargs.has_key('contours'): kwargs.pop('contours')
if kwargs.has_key('levels'):
levels= kwargs['levels']
kwargs.pop('levels')
elif contours:
if kwargs.has_key('cntrmass') and kwargs['cntrmass']:
levels= sc.linspace(0.,1.,_DEFAULTNCNTR)
elif True in sc.isnan(sc.array(X)):
levels= sc.linspace(sc.nanmin(X),sc.nanmax(X),_DEFAULTNCNTR)
else:
levels= sc.linspace(sc.amin(X),sc.amax(X),_DEFAULTNCNTR)
if kwargs.has_key('cntrmass') and kwargs['cntrmass']:
cntrmass= True
kwargs.pop('cntrmass')
else:
cntrmass= False
if kwargs.has_key('cntrmass'): kwargs.pop('cntrmass')
if kwargs.has_key('cntrcolors'):
cntrcolors= kwargs['cntrcolors']
kwargs.pop('cntrcolors')
elif contours:
cntrcolors='k'
if kwargs.has_key('cntrlabel') and kwargs['cntrlabel']:
cntrlabel= True
kwargs.pop('cntrlabel')
else:
cntrlabel= False
if kwargs.has_key('cntrlabel'): kwargs.pop('cntrlabel')
if kwargs.has_key('cntrlw'):
cntrlw= kwargs['cntrlw']
kwargs.pop('cntrlw')
elif contours:
cntrlw= None
if kwargs.has_key('cntrls'):
cntrls= kwargs['cntrls']
kwargs.pop('cntrls')
elif contours:
cntrls= None
if kwargs.has_key('cntrlabelsize'):
cntrlabelsize= kwargs['cntrlabelsize']
kwargs.pop('cntrlabelsize')
elif contours:
cntrlabelsize= None
if kwargs.has_key('cntrlabelcolors'):
cntrlabelcolors= kwargs['cntrlabelcolors']
kwargs.pop('cntrlabelcolors')
elif contours:
cntrlabelcolors= None
if kwargs.has_key('cntrinline'):
cntrinline= kwargs['cntrinline']
kwargs.pop('cntrinline')
elif contours:
cntrinline= None
if kwargs.has_key('retCumImage'):
retCumImage= kwargs['retCumImage']
kwargs.pop('retCumImage')
else:
retCumImage= False
if kwargs.has_key('colorbar'):
cb= kwargs['colorbar']
kwargs.pop('colorbar')
else:
cb= False
if kwargs.has_key('shrink'):
shrink= kwargs['shrink']
kwargs.pop('shrink')
else:
shrink= None
out= pyplot.imshow(X,extent=extent,**kwargs)
pyplot.axis(extent)
_add_axislabels(xlabel,ylabel)
_add_ticks()
#Add colorbar
if cb:
if shrink is None:
if kwargs.has_key('aspect'):
shrink= sc.amin([float(kwargs['aspect'])*0.87,1.])
else:
shrink= 0.87
CB1= pyplot.colorbar(out,shrink=shrink)
if not zlabel is None:
if zlabel[0] != '$':
thiszlabel=r'$'+zlabel+'$'
else:
thiszlabel=zlabel
CB1.set_label(zlabel)
if contours or retCumImage:
if kwargs.has_key('aspect'):
aspect= kwargs['aspect']
else:
aspect= None
if kwargs.has_key('origin'):
origin= kwargs['origin']
else:
origin= None
if cntrmass:
#Sum from the top down!
X[sc.isnan(X)]= 0.
sortindx= sc.argsort(X.flatten())[::-1]
cumul= sc.cumsum(sc.sort(X.flatten())[::-1])/sc.sum(X.flatten())
cntrThis= sc.zeros(sc.prod(X.shape))
cntrThis[sortindx]= cumul
cntrThis= sc.reshape(cntrThis,X.shape)
else:
cntrThis= X
if contours:
cont= pyplot.contour(cntrThis,levels,colors=cntrcolors,
linewidths=cntrlw,extent=extent,aspect=aspect,
linestyles=cntrls,origin=origin)
if cntrlabel:
pyplot.clabel(cont,fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
if retCumImage:
return cntrThis
else:
return out
def mcdata(data, other=None, x_offset=0, div=2, zero_line=True, events=None,
epochs=None, plot_handle=None, colours=None, title=None,
filename=None, show=True):
"""plot multichanneled data
-> general plot parameter
:type data: ndarray
:param data: The base data to plot with observations(samples) on the rows
and variables(channels) on the columns. This data will be plotted on in
the n topmost axes.
:type other: ndarray
:param other: Other data that augments the base data. The other data will
be plotted in one axe visibly divided from the base data.
Default=None
:type x_offset: int
:param x_offset: A offset value for the x-axis(samples). This allows for
the x-axis to show proper values for windows not starting at x=0. All
values for events and epochs etc. will not be shown if they do not
fall into the frame defined.
Default=0
:type div: float
:param div: Percentage of the figure height to use as divider for the
others plot.
Default=1
:type zero_line: bool
:param zero_line: if True, mark the zero line for the data channels
Default=True
:type events: dict
:param events: dict of events from [x_offset, x_offset+len(data)]. If the
dict entries are lists/ndarrays, vertical markers will be placed at
these samples. If the dict entries are tuples of length 2, like
(ndarray,ndarray), the first is interpreted as the waveform, and the
second as the events. Each unit will be coloured according to the
'colours' vector.
Default={}
:type epochs: dict
:param epochs: dict of epochs from [x_offset, x_offset+len(data)]. Epochs
with numeric keys will be interpreted as belonging to the unit with
that key and will be coloured according to the '' vector. All other
epochs will appear in grey colour. Epochs are passed as a 2dim vector,
like [[start,stop]].
Default={}
"""
# checks
if not isinstance(data, sp.ndarray):
raise ValueError("data is no ndarray!")
if data.ndim != 2:
raise ValueError("data is not dim=2!")
fig, ax = check_plotting_handle(plot_handle, create_ax=False)
# init
fig.clear()
has_other = other is not None
ns, nc = data.shape
x_vals = sp.arange(ns) + x_offset
if colours is None:
col_lst = COLOURS
elif colours == 'black':
col_lst = ['k'] * nc
else:
col_lst = colours
ax_spacer = div * 0.01
# prepare axes
if has_other:
ax_height = (0.8 - (nc + 1) * ax_spacer) / (nc + 1)
else:
ax_height = (0.8 - (nc - 1) * ax_spacer) / nc
for c in xrange(nc):
ax_size = (
0.1, 0.9 - (c + 1) * ax_height - c * ax_spacer, 0.8, ax_height)
ax = fig.add_axes(ax_size, sharex=ax, sharey=ax)
ax.set_ylabel('CH %d' % c)
if c != nc - 1:
plt.setp(ax.get_xticklabels(), visible=False)
#ax.set_xticklabels([tl.get_text() for tl in ax.get_xticklabels()], visible=False)
#ax.set_xlim(x_vals[0], x_vals[-1])
#ax.set_ylim(data.min() * 1.1, data.max() * 1.1)
if has_other:
ax = fig.add_axes((0.1, 0.1, 0.8, ax_height), sharex=ax)
ax.set_ylabel('OTHER')
#ax.set_xlim(x_vals[0], x_vals[-1])
#ax.set_ylim(-other.max() * 1.1, other.max() * 1.1)
# plot data
for c, a in enumerate(fig.axes[:nc]):
a.add_collection(
mpl.collections.LineCollection(
[sp.vstack((x_vals, data[:, c])).T], colors=[(0, 0, 0)]))
# plot other
if has_other:
fig.axes[-1].add_collection(mpl.collections.LineCollection(
[sp.vstack((x_vals, other[:, c])).T for c in
xrange(other.shape[1])], colors=col_lst))
# plot events
if events is not None:
for u in sorted(events):
try:
col = col_lst[u % len(col_lst)]
except:
col = 'gray'
if isinstance(events[u], tuple):
if len(events[u]) != 2:
raise ValueError('Event entry for unit %s is not a tuple '
'of length 2' % u)
u_wf, u_ev = events[u]
if not u_wf.shape[1] == nc:
raise ValueError('Waveform for unit %s has mismatching '
'channel count' % u)
cut = int(sp.floor(u_wf.shape[0] / 2.0))
for c, a in enumerate(fig.axes[:nc]):
a.add_collection(
mpl.collections.LineCollection(
[sp.vstack((sp.arange(u_wf.shape[0]) - cut + u_ev[i],
u_wf[:, c])).T
for i in xrange(u_ev.size)], colors=[col]))
if has_other:
for e in u_ev:
fig.axes[-1].axvline(e, c=col)
elif isinstance(events[u], (list, sp.ndarray)):
for a in fig.axes:
for e in events[u]:
a.axvline(e, c=col)
else:
raise ValueError('events for unit %s are messed up' % u)
# plot epochs
if epochs is not None:
for u in sorted(epochs):
try:
col = col_lst[u % len(col_lst)]
except:
col = 'gray'
for ep in epochs[u]:
for a in fig.axes:
a.axvspan(ep[0], ep[1], fc=col, alpha=0.2)
# zero lines
if zero_line:
for a in fig.axes:
a.add_collection(
mpl.collections.LineCollection(
[sp.vstack(([x_vals[0], x_vals[-1]], sp.zeros(2))).T],
linestyles='dashed',
colors=[(0, 0, 0)]))
# scale axes
fig.axes[0].set_xlim(x_vals[0], x_vals[-1])
fig.axes[0].set_ylim(sp.nanmin(data) * 1.05, sp.nanmax(data) * 1.05)
if has_other:
fig.axes[-1].set_ylim(sp.nanmin(other) * 1.1, sp.nanmax(other) * 1.1)
# figure title
if title is not None:
fig.suptitle(title)
# produce plot
if filename is not None:
save_figure(fig, filename, '')
if show is True:
plt.show()
# return
return fig
def initTau(self, groups, pa=1e-3, pb=1e-3, qa=1., qb=1., qE=None):
"""Method to initialise the precision of the noise
PARAMETERS
----------
pa: float
'a' parameter of the prior distribution
pb :float
'b' parameter of the prior distribution
qb: float
initialisation of the 'b' parameter of the variational distribution
qE: float
initial expectation of the variational distribution
"""
# Sanity checks
assert len(
groups
) == self.N, 'sample groups labels do not match number of samples'
tau_list = [None] * self.M
# convert groups into integers from 0 to n_groups
tmp = np.unique(groups, return_inverse=True)
groups_ix = tmp[1]
n_group = len(np.unique(groups_ix))
for m in range(self.M):
# Poisson noise model for count data
if self.lik[m] == "poisson":
tmp = 0.25 + 0.17 * s.nanmax(self.data[m], axis=0)
tmp = s.repeat(tmp[None, :], self.N, axis=0)
tau_list[m] = Tau_Seeger(dim=(self.N, self.D[m]), value=tmp)
# Bernoulli noise model for binary data
elif self.lik[m] == "bernoulli":
# tau_list[m] = Constant_Node(dim=(self.D[m],), value=s.ones(self.D[m])*0.25)
# tau_list[m] = Tau_Jaakkola(dim=(self.D[m],), value=0.25)
tau_list[m] = Tau_Jaakkola(dim=((self.N, self.D[m])), value=1.)
elif self.lik[m] == "zero_inflated":
# contains parameters to initialise both normal and jaakola tau
tau_list[m] = Zero_Inflated_Tau_Jaakkola(dim=((n_group,
self.D[m])),
value=1.,
pa=pa,
pb=pb,
qa=qa,
qb=qb,
groups=groups_ix,
qE=qE)
# Gaussian noise model for continuous data
elif self.lik[m] == "gaussian":
tau_list[m] = TauD_Node(dim=(n_group, self.D[m]),
pa=pa,
pb=pb,
qa=qa,
qb=qb,
groups=groups_ix,
qE=qE)
self.nodes["Tau"] = Multiview_Mixed_Node(self.M, *tau_list)
def TVDI_function(inNDVI,inLST,pas=0.02,t=1,s1Min=0.3,s2Max=0.8,ss1Min=0.2,ss2Max=0.8):
"""
Allows to calculates the TVDI.
this function is a modified version of the IDL script published by Monica Garcia:
(Garcia,M., Fernández, N., Villagarcía, L., Domingo, F., Puigdefábregas,J. & I. Sandholt. 2014. 2014.
Accuracy of the Temperature–Vegetation Dryness Index using MODIS under water-limited vs.
energy-limited evapotranspiration conditions Remote Sensing of Environment 149, 100-117.)
Input:
inNDVI: NDVI
inLST: land surface temperature
pas: intervall of the NDVI
S1min: lower threshold to determine the interval which will be used to determine the design parameters of LSTmax
S2max: upper threshold to determine the interval which will be used to determine the design parameters of LSTmax
ss1Min: lower threshold to determine the interval which will be used to determine the calculation of parmaètres LSTmin
ss2Max: upper threshold to determine the interval which will be used to determine the design parameters of LSTmin
t : t=0 to use Garcia M method and t=1 to calculate the TVDI without using the threshold .
Output:
TVDI
"""
TVDI=sp.zeros(inLST.shape)
if inNDVI.shape == inLST.shape :
inNdvi=sp.reshape(inNDVI,(inNDVI.size))
inLst=sp.reshape(inLST,(inLST.size))
mini=sp.nanmin(inNdvi) # valeur minimale
maxi=sp.nanmax(inNdvi) # valeur maximale
arg=sp.argsort(inNdvi) #trie et renvoi les indices des valeurs ordonnées
inV=inNdvi[arg] # on récupère les valeurs de NDVI
inT=inLst[arg] # on récupère les valeurs de temperature
# pas de decoupade du NDVI en intervalle
percentileMax=99.0
percentileMin=1.0
nObsMin=5 # la longeur minimale que doit avoir un intervalle pour être considéré
ni= int(round((maxi-mini)/pas ) + 1) # Nombre total d'intervalle
iValMax=0
iValMin=ni
#création des vecteurs de stockage
vx= sp.zeros((ni),dtype="float")
vMaxi=sp.zeros((ni),dtype="float")
vMini=sp.zeros((ni),dtype="float")
vMaxi[0:]=None
vMini[0:]=None
vNpi=sp.zeros((ni),dtype="float")
for k in range (ni):
hi=k*pas + mini # valeur de depart de l'intervalle
hs=k*pas + hi # valeur de fin de l'intervalle
a=sp.where(inV <= hi)
ii=a[0].max()
b=sp.where(inV <= hs)
iis=b[0].max()
vNpi[k]= iis - ii
inTp=inT[ii:iis+1] #recuperation des valeurs de temperature contenues dans cet intervalle
vx[k]=(hs - hi )/2 +hi #recuperation de valeur de NDVI qui se trouve au milieu intervalle
if vNpi[k] > nObsMin : #on teste si l'intervalle defini a suffisamment de valeur
inTp=inTp[sp.argsort(inTp)] #on trie les valeurs de temperature contenu dans cet intervalle
vMaxi[k]=inTp[ int( ( vNpi[k] *percentileMax/100 )) ] #on recupère la valeur de temperature qui correspond au 99em percentile de l'intervalle
vMini[k]=inTp[ int( ( vNpi[k] *percentileMin/100 ))] #on recupère la valeur de temperature qui correspond au 99em percentile de l'intervalle
if k >iValMax:
iValMax=k
if k < iValMin:
iValMin=k
# calcul de LSTmax et LSTmin
if (t==0):
# Dry Edge
# on utilise un seuil inferieur pour trouver la fin de l'intervalle qui va servir pour le calcul de la regression linéaire
# on utilise iValMin et iValMax pour eviter les nan c'est à dire on reste dans les intervalles qui respecte le nObsMin
try:
b=sp.where(vx < s1Min) # seuil inferieur à modifier
ii=sp.nanmax([sp.nanmax(b[0]),iValMin])
b=sp.where(vx < s2Max) # seuil superieur à modifier
iis=sp.nanmin([sp.nanmax(b[0]),iValMax])
# Wet Edge
c=sp.where(vx < ss1Min) # seuil inferieur à modifier
ii2=sp.nanmax([sp.nanmax(c[0]),iValMin])
c=sp.where(vx < ss2Max) # seuil superieur à modifier
iis2=sp.nanmin([sp.nanmax(c[0]),iValMax])
except:
print "problème avec les valeurs inferieures et superieures utilisées"
else:
ii=iValMin
iis=iValMax
ii2=iValMin
iis2=iValMax
#calcul de la regression linéaire
estimation1=sp.stats.linregress(vx[ii:iis+1],vMaxi[ii:iis+1])
#LSTmax= a * NDVI + b
lstmax_a=estimation1[0] #recuperation du paramètre de pente
lstmax_b=estimation1[1] #recuperation du paramètre de l'ordonnée à l'origine
estimation1=sp.stats.linregress(vx[ii2:iis2+1],vMini[ii2:iis2+1])
#LSTmax= a * NDVI + b
lstmin=sp.nanmin(vMini[ii2:iis2+1])
#calcul de TVDI
TVDI=( inLST - lstmin) / ( lstmax_b + (lstmax_a * inNDVI )- lstmin+0.00000001 )
# TVDI=( inLST - lstmin) / ( ( lstmax_b + (lstmax_a * inNDVI ))- lstmin +0.00001 )
else:
print "les deux tableaux n'ont pas la même taille"
exit
return TVDI
def plotbeamparametersv2(times,
configfile,
maindir,
fitdir='Fitted',
params=['Ne'],
filetemplate='params',
suptitle='Parameter Comparison',
werrors=False,
nelog=True):
"""
This function will plot the desired parameters for each beam along range.
The values of the input and measured parameters will be plotted
Inputs
Times - A list of times that will be plotted.
configfile - The INI file with the simulation parameters that will be useds.
maindir - The directory the images will be saved in.
params - List of Parameter names that will be ploted. These need to match
in the ionocontainer names.
filetemplate - The first part of a the file names.
suptitle - The supertitle for the plots.
werrors - A bools that determines if the errors will be plotted.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
# rc('text', usetex=True)
maindir = Path(maindir)
ffit = maindir / fitdir / 'fitteddata.h5'
inputfiledir = maindir / 'Origparams'
(sensdict, simparams) = readconfigfile(configfile)
paramslower = [ip.lower() for ip in params]
Nt = len(times)
Np = len(params)
#Read in fitted data
Ionofit = IonoContainer.readh5(str(ffit))
dataloc = Ionofit.Sphere_Coords
pnames = Ionofit.Param_Names
pnameslower = sp.array([ip.lower() for ip in pnames.flatten()])
p2fit = [
sp.argwhere(ip == pnameslower)[0][0] if ip in pnameslower else None
for ip in paramslower
]
time2fit = [None] * Nt
# Have to fix this because of time offsets
if times[0] == 0:
times += Ionofit.Time_Vector[0, 0]
for itn, itime in enumerate(times):
filear = sp.argwhere(Ionofit.Time_Vector[:, 0] >= itime)
if len(filear) == 0:
filenum = len(Ionofit.Time_Vector) - 1
else:
filenum = sp.argmin(sp.absolute(Ionofit.Time_Vector[:, 0] - itime))
time2fit[itn] = filenum
times_int = [Ionofit.Time_Vector[i] for i in time2fit]
# determine the beams
angles = dataloc[:, 1:]
rng = sp.unique(dataloc[:, 0])
b_arr = np.ascontiguousarray(angles).view(
np.dtype((np.void, angles.dtype.itemsize * angles.shape[1])))
_, idx, invidx = np.unique(b_arr, return_index=True, return_inverse=True)
beamlist = angles[idx]
Nb = beamlist.shape[0]
# Determine which imput files are to be used.
dirlist = sorted(inputfiledir.glob('*.h5'))
dirliststr = [str(i) for i in dirlist]
sortlist, outime, outfilelist, timebeg, timelist_s = IonoContainer.gettimes(
dirliststr)
timelist = timebeg.copy()
time2file = [None] * Nt
time2intime = [None] * Nt
# go through times find files and then times in files
for itn, itime in enumerate(times):
filear = sp.argwhere(timelist >= itime)
if len(filear) == 0:
filenum = [len(timelist) - 1]
else:
filenum = filear[0]
flist1 = []
timeinflist = []
for ifile in filenum:
filetimes = timelist_s[ifile]
log1 = (filetimes[:, 0] >= times_int[itn][0]) & (filetimes[:, 0] <
times_int[itn][1])
log2 = (filetimes[:, 1] > times_int[itn][0]) & (filetimes[:, 1] <=
times_int[itn][1])
log3 = (filetimes[:, 0] <= times_int[itn][0]) & (filetimes[:, 1] >
times_int[itn][1])
log4 = (filetimes[:, 0] > times_int[itn][0]) & (filetimes[:, 1] <
times_int[itn][1])
curtimes1 = sp.where(log1 | log2 | log3 | log4)[0].tolist()
flist1 = flist1 + [ifile] * len(curtimes1)
timeinflist = timeinflist + curtimes1
time2intime[itn] = timeinflist
time2file[itn] = flist1
nfig = int(sp.ceil(Nt * Nb))
imcount = 0
curfilenum = -1
# Loop for the figures
for i_fig in range(nfig):
lines = [None] * 2
labels = [None] * 2
(figmplf, axmat) = plt.subplots(int(sp.ceil(Np / 2)),
2,
figsize=(20, 15),
facecolor='w')
axvec = axmat.flatten()
# loop that goes through each axis loops through each parameter, beam
# then time.
for ax in axvec:
if imcount >= Nt * Nb * Np:
break
imcount_f = float(imcount)
itime = int(sp.floor(imcount_f / Nb / Np))
iparam = int(imcount_f / Nb - Np * itime)
ibeam = int(imcount_f - (itime * Np * Nb + iparam * Nb))
curbeam = beamlist[ibeam]
altlist = sp.sin(curbeam[1] * sp.pi / 180.) * rng
curparm = paramslower[iparam]
# Use Ne from input to compare the ne derived from the power.
if curparm == 'nepow':
curparm_in = 'ne'
else:
curparm_in = curparm
curcoord = sp.zeros(3)
curcoord[1:] = curbeam
for iplot, filenum in enumerate(time2file[itime]):
if curfilenum != filenum:
curfilenum = filenum
datafilename = dirlist[filenum]
Ionoin = IonoContainer.readh5(str(datafilename))
if ('ti' in paramslower) or ('vi' in paramslower):
Ionoin = maketi(Ionoin)
pnames = Ionoin.Param_Names
pnameslowerin = sp.array(
[ip.lower() for ip in pnames.flatten()])
prmloc = sp.argwhere(curparm_in == pnameslowerin)
if prmloc.size != 0:
curprm = prmloc[0][0]
# build up parameter vector bs the range values by finding the closest point in space in the input
curdata = sp.zeros(len(rng))
for irngn, irng in enumerate(rng):
curcoord[0] = irng
tempin = Ionoin.getclosestsphere(curcoord)[0][
time2intime[itime]]
Ntloc = tempin.shape[0]
tempin = sp.reshape(tempin, (Ntloc, len(pnameslowerin)))
curdata[irngn] = tempin[0, curprm]
#actual plotting of the input data
lines[0] = ax.plot(curdata,
altlist,
marker='o',
c='b',
linewidth=2)[0]
labels[0] = 'Input Parameters'
# Plot fitted data for the axis
indxkep = np.argwhere(invidx == ibeam)[:, 0]
curfit = Ionofit.Param_List[indxkep, time2fit[itime],
p2fit[iparam]]
rng_fit = dataloc[indxkep, 0]
alt_fit = rng_fit * sp.sin(curbeam[1] * sp.pi / 180.)
errorexist = 'n' + paramslower[iparam] in pnameslower
if errorexist and werrors:
eparam = sp.argwhere('n' +
paramslower[iparam] == pnameslower)[0][0]
curerror = Ionofit.Param_List[indxkep, time2fit[itime], eparam]
lines[1] = ax.errorbar(curfit,
alt_fit,
xerr=curerror,
fmt='-.',
c='g',
linewidth=2)[0]
else:
lines[1] = ax.plot(curfit,
alt_fit,
marker='o',
c='g',
linewidth=2)[0]
labels[1] = 'Fitted Parameters'
# get and plot the input data
numplots = len(time2file[itime])
# set the limit for the parameter
if curparm == 'vi':
ax.set(xlim=[
-1.25 * sp.nanmax(sp.absolute(curfit)), 1.25 *
sp.nanmax(sp.absolute(curfit))
])
elif curparm_in != 'ne':
ax.set(xlim=[
0.75 * sp.nanmin(curfit),
sp.minimum(1.25 * sp.nanmax(curfit), 8000.)
])
elif (curparm_in == 'ne') and nelog:
ax.set_xscale('log')
ax.set_xlabel(params[iparam])
ax.set_ylabel('Alt km')
ax.set_title(
'{0} vs Altitude, Time: {1}s Az: {2}$^o$ El: {3}$^o$'.format(
params[iparam], times[itime], *curbeam))
imcount += 1
# save figure
figmplf.suptitle(suptitle, fontsize=20)
if None in labels:
labels.remove(None)
lines.remove(None)
plt.figlegend(lines,
labels,
loc='lower center',
ncol=5,
labelspacing=0.)
fname = filetemplate + '_{0:0>3}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
def bovy_dens2d(X, **kwargs):
"""
NAME:
bovy_dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
colorbar - if True, add colorbar
shrink= colorbar argument: shrink the colorbar by the factor (optional)
conditional - normalize each column separately (for probability densities, i.e., cntrmass=True)
gcf=True does not start a new figure (does change the ranges and labels)
Contours:
justcontours - if True, only draw contours
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
cntrSmooth - use ndimage.gaussian_filter to smooth before contouring
onedhists - if True, make one-d histograms on the sides
onedhistcolor - histogram color
retAxes= return all Axes instances
retCont= return the contour instance
OUTPUT:
plot to output device, Axes instances depending on input
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
overplot = kwargs.pop('overplot', False)
gcf = kwargs.pop('gcf', False)
if not overplot and not gcf:
pyplot.figure()
xlabel = kwargs.pop('xlabel', None)
ylabel = kwargs.pop('ylabel', None)
zlabel = kwargs.pop('zlabel', None)
if 'extent' in kwargs:
extent = kwargs.pop('extent')
else:
xlimits = kwargs.pop('xrange', [0, X.shape[1]])
ylimits = kwargs.pop('yrange', [0, X.shape[0]])
extent = xlimits + ylimits
if not 'aspect' in kwargs:
kwargs['aspect'] = (xlimits[1] - xlimits[0]) / float(ylimits[1] -
ylimits[0])
noaxes = kwargs.pop('noaxes', False)
justcontours = kwargs.pop('justcontours', False)
if ('contours' in kwargs and kwargs['contours']) or \
'levels' in kwargs or justcontours or \
('cntrmass' in kwargs and kwargs['cntrmass']):
contours = True
else:
contours = False
kwargs.pop('contours', None)
if 'levels' in kwargs:
levels = kwargs['levels']
kwargs.pop('levels')
elif contours:
if 'cntrmass' in kwargs and kwargs['cntrmass']:
levels = sc.linspace(0., 1., _DEFAULTNCNTR)
elif True in sc.isnan(sc.array(X)):
levels = sc.linspace(sc.nanmin(X), sc.nanmax(X), _DEFAULTNCNTR)
else:
levels = sc.linspace(sc.amin(X), sc.amax(X), _DEFAULTNCNTR)
cntrmass = kwargs.pop('cntrmass', False)
conditional = kwargs.pop('conditional', False)
cntrcolors = kwargs.pop('cntrcolors', 'k')
cntrlabel = kwargs.pop('cntrlabel', False)
cntrlw = kwargs.pop('cntrlw', None)
cntrls = kwargs.pop('cntrls', None)
cntrSmooth = kwargs.pop('cntrSmooth', None)
cntrlabelsize = kwargs.pop('cntrlabelsize', None)
cntrlabelcolors = kwargs.pop('cntrlabelcolors', None)
cntrinline = kwargs.pop('cntrinline', None)
retCumImage = kwargs.pop('retCumImage', False)
cb = kwargs.pop('colorbar', False)
shrink = kwargs.pop('shrink', None)
onedhists = kwargs.pop('onedhists', False)
onedhistcolor = kwargs.pop('onedhistcolor', 'k')
retAxes = kwargs.pop('retAxes', False)
retCont = kwargs.pop('retCont', False)
if onedhists:
if overplot or gcf: fig = pyplot.gcf()
else: fig = pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
axHistx = pyplot.axes(rect_histx)
axHisty = pyplot.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
ax = pyplot.gca()
ax.set_autoscale_on(False)
if conditional:
plotthis = X / sc.tile(sc.sum(X, axis=0), (X.shape[1], 1))
else:
plotthis = X
if not justcontours:
out = pyplot.imshow(plotthis, extent=extent, **kwargs)
if not overplot:
pyplot.axis(extent)
_add_axislabels(xlabel, ylabel)
_add_ticks()
#Add colorbar
if cb and not justcontours:
if shrink is None:
shrink = sc.amin([float(kwargs.pop('aspect', 1.)) * 0.87, 1.])
CB1 = pyplot.colorbar(out, shrink=shrink)
if not zlabel is None:
if zlabel[0] != '$':
thiszlabel = r'$' + zlabel + '$'
else:
thiszlabel = zlabel
CB1.set_label(thiszlabel)
if contours or retCumImage:
aspect = kwargs.get('aspect', None)
origin = kwargs.get('origin', None)
if cntrmass:
#Sum from the top down!
plotthis[sc.isnan(plotthis)] = 0.
sortindx = sc.argsort(plotthis.flatten())[::-1]
cumul = sc.cumsum(sc.sort(plotthis.flatten())[::-1]) / sc.sum(
plotthis.flatten())
cntrThis = sc.zeros(sc.prod(plotthis.shape))
cntrThis[sortindx] = cumul
cntrThis = sc.reshape(cntrThis, plotthis.shape)
else:
cntrThis = plotthis
if contours:
if not cntrSmooth is None:
cntrThis = ndimage.gaussian_filter(cntrThis,
cntrSmooth,
mode='nearest')
cont = pyplot.contour(cntrThis,
levels,
colors=cntrcolors,
linewidths=cntrlw,
extent=extent,
aspect=aspect,
linestyles=cntrls,
origin=origin)
if cntrlabel:
pyplot.clabel(cont,
fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
#Add onedhists
if not onedhists:
if retCumImage:
return cntrThis
elif retAxes:
return pyplot.gca()
elif retCont:
return cont
elif justcontours:
return cntrThis
else:
return out
histx = sc.nansum(X.T, axis=1) * m.fabs(ylimits[1] - ylimits[0]) / X.shape[
1] #nansum bc nan is *no dens value*
histy = sc.nansum(X.T,
axis=0) * m.fabs(xlimits[1] - xlimits[0]) / X.shape[0]
histx[sc.isnan(histx)] = 0.
histy[sc.isnan(histy)] = 0.
dx = (extent[1] - extent[0]) / float(len(histx))
axHistx.plot(sc.linspace(extent[0] + dx, extent[1] - dx, len(histx)),
histx,
drawstyle='steps-mid',
color=onedhistcolor)
dy = (extent[3] - extent[2]) / float(len(histy))
axHisty.plot(histy,
sc.linspace(extent[2] + dy, extent[3] - dy, len(histy)),
drawstyle='steps-mid',
color=onedhistcolor)
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axHistx.set_ylim(0, 1.2 * sc.amax(histx))
axHisty.set_xlim(0, 1.2 * sc.amax(histy))
if retCumImage:
return cntrThis
elif retAxes:
return (axScatter, axHistx, axHisty)
elif justcontours:
return cntrThis
else:
return out
def bovy_dens2d(X, **kwargs):
"""
NAME:
bovy_dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
colorbar - if True, add colorbar
shrink= colorbar argument: shrink the colorbar by the factor (optional)
Contours:
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels
are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
onedhists - if True, make one-d histograms on the sides
onedhistcolor - histogram color
retAxes= return all Axes instances
OUTPUT:
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
if kwargs.has_key('overplot'):
overplot = kwargs['overplot']
kwargs.pop('overplot')
else:
overplot = False
if not overplot:
pyplot.figure()
if kwargs.has_key('xlabel'):
xlabel = kwargs['xlabel']
kwargs.pop('xlabel')
else:
xlabel = None
if kwargs.has_key('ylabel'):
ylabel = kwargs['ylabel']
kwargs.pop('ylabel')
else:
ylabel = None
if kwargs.has_key('zlabel'):
zlabel = kwargs['zlabel']
kwargs.pop('zlabel')
else:
zlabel = None
if kwargs.has_key('extent'):
extent = kwargs['extent']
kwargs.pop('extent')
else:
if kwargs.has_key('xrange'):
xlimits = list(kwargs['xrange'])
kwargs.pop('xrange')
else:
xlimits = [0, X.shape[0]]
if kwargs.has_key('yrange'):
ylimits = list(kwargs['yrange'])
kwargs.pop('yrange')
else:
ylimits = [0, X.shape[1]]
extent = xlimits + ylimits
if not kwargs.has_key('aspect'):
kwargs['aspect'] = (xlimits[1] - xlimits[0]) / float(ylimits[1] -
ylimits[0])
if kwargs.has_key('noaxes'):
noaxes = kwargs['noaxes']
kwargs.pop('noaxes')
else:
noaxes = False
if (kwargs.has_key('contours') and kwargs['contours']) or \
kwargs.has_key('levels') or \
(kwargs.has_key('cntrmass') and kwargs['cntrmass']):
contours = True
else:
contours = False
if kwargs.has_key('contours'): kwargs.pop('contours')
if kwargs.has_key('levels'):
levels = kwargs['levels']
kwargs.pop('levels')
elif contours:
if kwargs.has_key('cntrmass') and kwargs['cntrmass']:
levels = sc.linspace(0., 1., _DEFAULTNCNTR)
elif True in sc.isnan(sc.array(X)):
levels = sc.linspace(sc.nanmin(X), sc.nanmax(X), _DEFAULTNCNTR)
else:
levels = sc.linspace(sc.amin(X), sc.amax(X), _DEFAULTNCNTR)
if kwargs.has_key('cntrmass') and kwargs['cntrmass']:
cntrmass = True
kwargs.pop('cntrmass')
else:
cntrmass = False
if kwargs.has_key('cntrmass'): kwargs.pop('cntrmass')
if kwargs.has_key('cntrcolors'):
cntrcolors = kwargs['cntrcolors']
kwargs.pop('cntrcolors')
elif contours:
cntrcolors = 'k'
if kwargs.has_key('cntrlabel') and kwargs['cntrlabel']:
cntrlabel = True
kwargs.pop('cntrlabel')
else:
cntrlabel = False
if kwargs.has_key('cntrlabel'): kwargs.pop('cntrlabel')
if kwargs.has_key('cntrlw'):
cntrlw = kwargs['cntrlw']
kwargs.pop('cntrlw')
elif contours:
cntrlw = None
if kwargs.has_key('cntrls'):
cntrls = kwargs['cntrls']
kwargs.pop('cntrls')
elif contours:
cntrls = None
if kwargs.has_key('cntrlabelsize'):
cntrlabelsize = kwargs['cntrlabelsize']
kwargs.pop('cntrlabelsize')
elif contours:
cntrlabelsize = None
if kwargs.has_key('cntrlabelcolors'):
cntrlabelcolors = kwargs['cntrlabelcolors']
kwargs.pop('cntrlabelcolors')
elif contours:
cntrlabelcolors = None
if kwargs.has_key('cntrinline'):
cntrinline = kwargs['cntrinline']
kwargs.pop('cntrinline')
elif contours:
cntrinline = None
if kwargs.has_key('retCumImage'):
retCumImage = kwargs['retCumImage']
kwargs.pop('retCumImage')
else:
retCumImage = False
if kwargs.has_key('colorbar'):
cb = kwargs['colorbar']
kwargs.pop('colorbar')
else:
cb = False
if kwargs.has_key('shrink'):
shrink = kwargs['shrink']
kwargs.pop('shrink')
else:
shrink = None
if kwargs.has_key('onedhists'):
onedhists = kwargs['onedhists']
kwargs.pop('onedhists')
else:
onedhists = False
if kwargs.has_key('onedhistcolor'):
onedhistcolor = kwargs['onedhistcolor']
kwargs.pop('onedhistcolor')
else:
onedhistcolor = 'k'
if kwargs.has_key('retAxes'):
retAxes = kwargs['retAxes']
kwargs.pop('retAxes')
else:
retAxes = False
if onedhists:
if overplot: fig = pyplot.gcf()
else: fig = pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
axHistx = pyplot.axes(rect_histx)
axHisty = pyplot.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
ax = pyplot.gca()
ax.set_autoscale_on(False)
out = pyplot.imshow(X, extent=extent, **kwargs)
pyplot.axis(extent)
_add_axislabels(xlabel, ylabel)
_add_ticks()
#Add colorbar
if cb:
if shrink is None:
if kwargs.has_key('aspect'):
shrink = sc.amin([float(kwargs['aspect']) * 0.87, 1.])
else:
shrink = 0.87
CB1 = pyplot.colorbar(out, shrink=shrink)
if not zlabel is None:
if zlabel[0] != '$':
thiszlabel = r'$' + zlabel + '$'
else:
thiszlabel = zlabel
CB1.set_label(zlabel)
if contours or retCumImage:
if kwargs.has_key('aspect'):
aspect = kwargs['aspect']
else:
aspect = None
if kwargs.has_key('origin'):
origin = kwargs['origin']
else:
origin = None
if cntrmass:
#Sum from the top down!
X[sc.isnan(X)] = 0.
sortindx = sc.argsort(X.flatten())[::-1]
cumul = sc.cumsum(sc.sort(X.flatten())[::-1]) / sc.sum(X.flatten())
cntrThis = sc.zeros(sc.prod(X.shape))
cntrThis[sortindx] = cumul
cntrThis = sc.reshape(cntrThis, X.shape)
else:
cntrThis = X
if contours:
cont = pyplot.contour(cntrThis,
levels,
colors=cntrcolors,
linewidths=cntrlw,
extent=extent,
aspect=aspect,
linestyles=cntrls,
origin=origin)
if cntrlabel:
pyplot.clabel(cont,
fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
#Add onedhists
if not onedhists:
if retCumImage:
return cntrThis
elif retAxes:
return pyplot.gca()
else:
return out
histx = sc.nansum(X.T, axis=1) * m.fabs(ylimits[1] - ylimits[0]) / X.shape[
1] #nansum bc nan is *no dens value*
histy = sc.nansum(X.T,
axis=0) * m.fabs(xlimits[1] - xlimits[0]) / X.shape[0]
histx[sc.isnan(histx)] = 0.
histy[sc.isnan(histy)] = 0.
dx = (extent[1] - extent[0]) / float(len(histx))
axHistx.plot(sc.linspace(extent[0] + dx, extent[1] - dx, len(histx)),
histx,
drawstyle='steps-mid',
color=onedhistcolor)
dy = (extent[3] - extent[2]) / float(len(histy))
axHisty.plot(histy,
sc.linspace(extent[2] + dy, extent[3] - dy, len(histy)),
drawstyle='steps-mid',
color=onedhistcolor)
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axHistx.set_ylim(0, 1.2 * sc.amax(histx))
axHisty.set_xlim(0, 1.2 * sc.amax(histy))
if retCumImage:
return cntrThis
elif retAxes:
return (axScatter, axHistx, axHisty)
else:
return out
