def run_bootstrap_correlations(Xs, Ys, num_perms, plot=False, verbose=False, permute=False):
# Returns the difference of correlation between X[1]*Y[1] - X[0]*Y[0] using bootstrap
corrs = []
pvals = []
boot_corrs = []
# need to define ahead of time the bootstrap indexes so we run the same ones for X[1] and X[0] (e.g. baseline and last)
num_subjects = Xs[0].shape[0]
boot_idx = np.random.random_integers(0, num_subjects-1, [num_perms, num_subjects])
for X, Y in zip(Xs, Ys):
corr = np.empty([X.shape[1], Y.shape[1]])
pval = np.empty([X.shape[1], Y.shape[1]])
for x in range(X.shape[1]):
for y in range(Y.shape[1]):
corr[x, y], pval[x, y] = stats.pearsonr(X[:, x], Y[:, y])
corrs.append(corr)
pvals.append(pval)
if plot:
plot_correlations(corr)
pl.title('n=%d'%X.shape[0])
pl.clim(-.2, .8)
pl.draw()
# checking p-values of the differences
boot_res = do_bootstrapping(X, Y, num_perms, verbose, reuse_ids=boot_idx)
boot_corrs.append(boot_res)
dcorr = boot_corrs[1] - boot_corrs[0]
return (corrs, pvals, dcorr)
def plot_onereg(self, regid):
"""Plot mintime of connectivity for one individual region"""
tomat = self.llat * np.nan
for i,v in enumerate(self.mintmat[1:,regid]):
tomat[self.regmat==(i+1)] = v
frmat = self.llat * np.nan
for i,v in enumerate(self.mintmat[regid,1:]):
frmat[self.regmat==(i+1)] = v
djtk,lim = djdticks(max(np.nanmax(tomat), np.nanmax(frmat)))
figpref.current()
pl.close(1)
fig = pl.figure(1,(8,8))
pl.clf()
pl.suptitle("Connectivity for region %i" % regid)
mask = self.regmat == regid
x,y = self.gcm.mp(self.llon[mask], self.llat[mask])
pl.subplots_adjust(hspace=0, top=0.95,bottom = 0.15)
pl.subplot(2,1,1, axisbg="0.8")
self.gcm.pcolor(frmat, cmap=WRY(), rasterized=True)
pl.clim(0,lim)
self.gcm.mp.text(70,60,'Time from region')
self.gcm.mp.scatter(x,y,5,'b')
pl.subplot(2,1,2, axisbg="0.8")
self.gcm.pcolor(tomat, cmap=WRY(), rasterized=True)
pl.clim(0,lim)
self.gcm.mp.text(70,60,'Time to region')
self.gcm.mp.scatter(x,y,5,'b')
if 'mycolor' in sys.modules:
mycolor.freecbar([0.2,0.12,0.6,0.025], djtk, cmap=WRY())
pl.savefig('figs/onereg_%02i_%02i_%06i.png' %
(self.regdi, self.regdj, regid))
def drawplot(self, dates, hrs, observability):
"""Generate the observability figure.
"""
if self.dt == 0:
ylabel = 'UTC Time'
else:
ylabel = 'UTC +{}'.format(self.dt)
observer = ephem.Observer()
ddates = []
for d in dates:
observer.date = str(d)
ddates.append(observer.date.datetime())
fig = py.figure()
axpos = [.1, .12, .77, .76]
ax = py.subplot(111, position=axpos)
py.contourf(ddates, hrs, observability, cmap=py.cm.Greens)
py.clim(0, 1.5)
months = mdates.MonthLocator() # every month
ax.xaxis.set_major_locator(months)
hfmt = ddates.DateFormatter('%b ')
ax.xaxis.set_major_formatter(hfmt)
yt = np.array([0, 4, 8, 12, 16, 20, 24])
title = '%s: RA = %s, DEC = %s\nalt > %1.1f, sun < -%1.1f' % \
(self.obs, str(self.ra), str(self.dec), self.minElev, self.twilight)
ax.set_title(title, fontsize=self.fs * 1.2)
ax.set_yticks(yt)
ax.yaxis.set_ticks(range(25), minor=True)
ax.set_yticklabels(yt % 24)
ax.set_ylabel(ylabel, fontsize=self.fs)
ax.grid(axis='x')
ax2 = py.twinx()
ax2.set_position(axpos)
yt2 = np.arange(-24, 24, 3)
yt2 = yt2[(yt2 >= dt) * (yt2 <= (24 + dt))]
ax2.set_ylim(dt, 24 + dt)
ax2.set_yticks(yt2)
ax2.set_ylabel('Local Time: UTC %+1.1f' % dt, fontsize=fs)
ax2.grid(axis='y')
tlabs = yt2 % 12
tlabs = []
for yt in yt2:
if (yt % 24) == 12:
lab = 'noon'
elif (yt % 24) == 0:
lab = 'mdnt'
elif (yt % 24) >= 12:
lab = '%i pm' % (yt % 12)
else:
lab = '%i am' % (yt % 12)
tlabs.append(lab)
ax2.set_yticklabels(tlabs)
ax2.yaxis.set_ticks(range(dt, dt + 25), minor=True)
self.fig = fig
def movie(self):
import matplotlib as mpl
mpl.rcParams['axes.labelcolor'] = 'white'
pl.close(1)
pl.figure(1, (8, 4.5), facecolor='k')
miv = np.ma.masked_invalid
figpref.current()
jd0 = pl.date2num(dtm(2005, 1, 1))
jd1 = pl.date2num(dtm(2005, 12, 31))
mp = projmaps.Projmap('glob')
x, y = mp(self.llon, self.llat)
for t in np.arange(jd0, jd1):
print pl.num2date(t)
self.load(t)
pl.clf()
pl.subplot(111, axisbg='k')
mp.pcolormesh(x,
y,
miv(np.sqrt(self.u**2 + self.v**2)),
cmap=cm.gist_heat)
pl.clim(0, 1.5)
mp.nice()
pl.title(
'%04i-%02i-%02i' % (pl.num2date(t).year, pl.num2date(t).month,
pl.num2date(t).day),
color='w')
pl.savefig('/Users/bror/oscar/norm/%03i.png' % t,
bbox_inches='tight',
facecolor='k',
dpi=150)
def plot_figs():
pylab.subplot(221, aspect="equal")
X, Y = pylab.meshgrid(list(range(ins.num_atoms)), list(range(ins.num_atoms)))
pylab.pcolor(X, Y, ins.norm_cross_correlation)
pylab.colorbar()
pylab.clim(-0.15, 0.15)
pylab.title("Cross Correlations")
pylab.subplot(222)
pylab.plot(pdb_Bfactors, "bo-", label="ex.")
pylab.plot(ins.Bfactors, "ro-", label="calc.")
pylab.legend()
pylab.xlabel("Residue")
# pylab.ylabel("a.u.")
pylab.title("B factors")
pylab.grid()
pylab.subplot(223, aspect="equal")
X, Y = pylab.meshgrid(list(range(ins.num_atoms)), list(range(ins.num_atoms)))
pylab.pcolor(X, Y, ins.adj_mat)
pylab.colorbar()
pylab.title("Adjacency Mat.")
pylab.subplot(224)
pylab.plot(ins.graph_eigvec[:, 1], "go-")
pylab.xlabel("Residue")
pylab.grid()
pylab.show()
def draw_astar_graph(obs_map, graph):
"""Debugging function which will show the current state of the m*
graph
"""
# Draw the obstacles
pylab.hold(False)
sizex = len(obs_map)
sizey = len(obs_map[0])
pylab.plot([-.5, sizex - .5, sizex - .5, -.5, -.5],
[-.5, -.5, sizey - .5, sizey - .5, -.5], 'k')
pylab.hold(True)
pylab.matshow((1 - numpy.array(obs_map)).T, fignum=0)
pylab.gray()
pylab.clim(-2, 1)
# Assemble the quiver
X, Y, U, V = [[] for i in xrange(4)]
for node in graph.graph.itervalues():
x, y = node.coord
u, v = map(lambda w, v: w - v, node.policy, node.coord)
X.append(x)
Y.append(y)
U.append(u)
V.append(v)
pylab.quiver(X, Y, U, V)
pylab.xlim([-.5, sizex - .5])
pylab.ylim([-.5, sizey - .5])
pylab.xticks([])
pylab.yticks([])
pylab.hold(False)
pylab.show()
def plot_pairwise_corrcoef(data,ranklist=range(16,24),title="Correlation Coefficient"):
array_byAttribRankRange=[]
for attrib in attribList:
array_byAttribRankRange.append(get_byAttribRankRange(data, attrib=attrib, ranklist=ranklist))
Narray = len(array_byAttribRankRange)
array_corrcoef=np.zeros((Narray,Narray),dtype='float')
for i,elemi in enumerate(array_byAttribRankRange[::-1]):
for j,elemj in enumerate(array_byAttribRankRange[::-1]):
if i>j:
continue
elif i==j:
array_corrcoef[i,j]=1
else:
array_corrcoef[i,j]=np.corrcoef(elemi,elemj)[0,1]
P.pcolor(np.transpose(array_corrcoef), cmap=P.cm.RdBu, alpha=0.8)
P.title(title)
P.xlim([0,23])
P.ylim([0,23])
P.clim([-1,1])
P.xticks(range(len(attribList)), attribListAbrv[::-1],rotation='vertical')
P.yticks(range(len(attribList)), attribListAbrv[::-1])
P.subplots_adjust(bottom=0.35)
P.subplots_adjust(left=0.25)
P.colorbar()
return array_corrcoef
def movie(self):
import matplotlib as mpl
mpl.rcParams['axes.labelcolor'] = 'white'
pl.close(1)
pl.figure(1,(8,4.5),facecolor='k')
miv = np.ma.masked_invalid
figpref.current()
jd0 = pl.date2num(dtm(2005,1,1))
jd1 = pl.date2num(dtm(2005,12,31))
mp = projmaps.Projmap('glob')
x,y = mp(self.llon,self.llat)
for t in np.arange(jd0,jd1):
print pl.num2date(t)
self.load(t)
pl.clf()
pl.subplot(111,axisbg='k')
mp.pcolormesh(x,y,
miv(np.sqrt(self.u**2 +self.v**2)),
cmap=cm.gist_heat)
pl.clim(0,1.5)
mp.nice()
pl.title('%04i-%02i-%02i' % (pl.num2date(t).year,
pl.num2date(t).month,
pl.num2date(t).day),
color='w')
pl.savefig('/Users/bror/oscar/norm/%03i.png' % t,
bbox_inches='tight',facecolor='k',dpi=150)
def plot_preds_orient(weights, plt_num=[1, 1, 1]):
orients = ['Mean Horiz', 'Mean Vert', 'Mean Diag', 'Mean Diag 2']
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
x = 7
mid = x / 2
vals_hor = np.zeros([x, x])
vals_ver = np.zeros([x, x])
vals_diag = np.zeros([x, x])
delta = (mid / 2)
vals_hor[mid, [mid - delta, mid + delta]] = 1
vals_ver[[mid - delta, mid + delta], mid] = 1
x, y = np.diag_indices_from(vals_diag)
vals_diag[x[mid - delta], y[mid - delta]] = 1
vals_diag[x[mid + delta], y[mid + delta]] = 1
vals_diag2 = np.array(zip(*vals_diag[::-1]))
# delta = mid - mid / 2
# vals_hor[mid, [mid - delta, mid + delta]] = 1
# vals_ver[:, mid] = 1
# np.fill_diagonal(vals_diag, 1)
# np.fill_diagonal(vals_diag2, 1)
# vals_diag2 = np.array(zip(*vals_diag2[::-1]))
dat = (vals_hor * weights[0] + vals_ver * weights[1] + vals_diag
* weights[2] + vals_diag2 * weights[3])
dat = reverse_fft(dat)
ext = mid
_ = plt.imshow(dat, cmap=colors.CoolWarm,
interpolation='bilinear',
extent=(-ext, ext, -ext, ext))
ylims = np.array([0, np.abs(dat).max()])
plt.clim(ylims)
plt.colorbar()
adjust_spines(ax, [])
def plot_color_map_2d( data, interpolation = None, limits = None,
axis_labels = None, title = None, show = False ):
"""
interpolation = None will default to using interpolation
interpolation = 'none' will ensure no interpolation is performed
"""
assert data.ndim == 2
p = pylab.imshow( data, interpolation = interpolation )
fig = pylab.gcf()
pylab.clim() # clamp the color limits
pylab.colorbar() # add color bar to indicate scale
if limits is not None:
xlim = limits[:2]
ylim = limits[2:]
num_ticks = 11
M, N = data.shape
xticks = numpy.linspace( 0, M-1, num_ticks )
yticks = numpy.linspace( 0, N-1, num_ticks )
pylab.xticks( xticks, numpy.linspace( xlim[0], xlim[1], num_ticks ) )
pylab.yticks( yticks, numpy.linspace( ylim[0], ylim[1], num_ticks ) )
if axis_labels is not None:
p.set_xlabel( axis_labels[0] )
p.set_ylabel( axis_labels[1] )
if title is not None:
pylab.title( title )
if show:
pylab.show()
def showqtresultfit(thk, wc, t2, datvec, resp, t,
islog=True, clim=None, nu=3, nv=2):
''' show mrs qt result and data fit
showqtresultfit(thk,wc,t2,datvec,resp,t,islog=True,clim=None,nu=3,nv=2)
'''
if clim is None:
cma = max(datvec)
cmi = min(datvec)
if islog:
cma = N.log10(cma)
cmi = cma - 1.5
clim = (cmi, cma)
nt = len(t)
nq = len(datvec) / nt
si = (nq, nt)
# P.clf()
# P.subplot(nu,nv,1)
fig = P.figure(1)
ax1 = fig.add_subplot(nu, nv, 1)
draw1dmodel(wc, thk, islog=False, xlab=r'$\theta$')
# P.subplot(nu,nv,3)
ax3 = fig.add_subplot(nu, nv, 3)
draw1dmodel(t2, thk, xlab='T2* in ms')
ax3.set_xticks( [0.02, 0.05, 0.1, 0.2, 0.5] )
ax3.set_xticklabels( ('0.02', '0.05', '0.1', '0.2', '0.5') )
# P.subplot(nu,nv,2)
ax2 = fig.add_subplot(nu, nv, 2)
if islog:
P.imshow(N.log10( N.array(datvec).reshape(si) ),
interpolation='nearest', aspect='auto')
else:
P.imshow(N.array(datvec).reshape(si),
interpolation='nearest', aspect='auto')
P.clim(clim)
# P.subplot(nu,nv,4)
ax4 = fig.add_subplot(nu, nv, 4)
if islog:
P.imshow(N.log10(resp.reshape(si)),
interpolation='nearest',aspect='auto')
else:
P.imshow(resp.reshape(si),
interpolation='nearest',aspect='auto')
misfit = N.array( datvec - resp )
P.clim(clim)
# P.subplot(nu,nv,5)
ax5 = fig.add_subplot(nu, nv, 5)
P.hist(misfit, bins=30)
P.axis('tight')
P.grid(which='both')
P.text(P.xlim()[0], N.mean( P.ylim() ),
' std=%g nV' % rndig( N.std(misfit), 3 ) )
# P.subplot(nu,nv,6)
ax6 = fig.add_subplot(nu, nv, 6)
P.imshow(misfit.reshape(si), interpolation='nearest', aspect='auto')
ax = [ ax1, ax2, ax3, ax4, ax5, ax6 ]
return ax
def plotLayerwiseStates(states, idx=None):
from utils.mscfuncs import imagesc
import pylab
if idx == None:
idx = range(len(states))
mx = 0.
for i in idx:
mx = numpy.max([states[i].X.max(), mx])
handle_lst = []
for i in idx:
handle_lst.append(pylab.figure())
for lay in range(states[0].X.shape[2]):
ax = pylab.subplot(3, 3, lay + 1)
imagesc(states[i].X[:, :, lay])
pylab.title(
'layer %s (%d) (t=%1.3fs)' %
(states[i].layers[states[i].layeridx[lay]], lay, states[i].t),
fontsize=8)
pylab.clim([0, mx])
pylab.setp(ax, 'xticklabels', [])
pylab.setp(ax, 'yticklabels', [])
return handle_lst
def __call__(self, n):
if len(self.f.shape) == 3:
# f = f[x,v,t], 2 dim in phase space
ft = self.f[n,:,:]
pylab.pcolormesh(self.X, self.V, ft.T, cmap = 'jet')
pylab.colorbar()
pylab.clim(0,0.38) # for Landau test case
pylab.grid()
pylab.axis([self.xmin, self.xmax, self.ymin, self.ymax])
pylab.xlabel('$x$', fontsize = 18)
pylab.ylabel('$v$', fontsize = 18)
pylab.title('$N_x$ = %d, $N_v$ = %d, $t$ = %2.1f' % (self.x.N, self.v.N, self.it*self.t.width))
pylab.savefig(self.path + self.filename)
pylab.clf()
return None
if len(self.f.shape) == 2:
# f = f[x], 1 dim in phase space
ft = self.f[n,:]
pylab.plot(self.x.gridvalues,ft,'ob')
pylab.grid()
pylab.axis([self.xmin, self.xmax, self.ymin, self.ymax])
pylab.xlabel('$x$', fontsize = 18)
pylab.ylabel('$f(x)$', fontsize = 18)
pylab.savefig(self.path + self.filename)
return None
def plot_shear_stress_grid(self, grid):
'''
This method must follow a call to the flow_across_grid() method
and requires the instance of the raster grid which was created
when the DEM is read in.
Shear stress is calculated at each interior node, to correspond with
where water depths are calculated (interior nodes).
This takes that node vector, converts it into a raster.
Presently, all nodes the colorbar is blocked at intervals of ten, using
the following function call:
>>> cmap = plt.get_cmap('Spectral', 10)
To change the interval at which color blocks are set, the value of 10 can
be changed.
To specify a particular range of shear stress values, the following call
can be added to the function. Say we want the low values on the color bar to start at 10
and increase to 100, which is currently the default setting:
>>> pylab.clim(vmin=10, vmax=100)
The colorbar is plotted using a blocked color map like those found here:
http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
The Spectral map has small values starting at shades of pink while increasing
values go over the color spectrum from orange, to yellow, to green, to blue and
purple as the values increase.
'''
# First we create a new figure window to plot within
plt.figure('Shear Stress raster')
# And then we convert the shear stress node vector to raster format
tn = grid.node_vector_to_raster(self.tau)
# This call selects the color map and interval which it will be blocked by
cmap = plt.get_cmap('Spectral', 10)
# This sets the minimum on the color bar, anything beneath the lower clim() value is set to white.
cmap.set_under('white')
# Creating the grid image to show
im2 = pylab.imshow(tn,
cmap=cmap,
extent=[
0, grid.number_of_node_columns * grid.dx, 0,
grid.number_of_node_rows * grid.dx
])
# Creating the colorbar for the grid image
cb = pylab.colorbar(im2)
# Sets the minimum and maximum values for the colorbar.
pylab.clim(vmin=10, vmax=100)
# Set titles for the figure and colorbar.
cb.set_label('Shear Stress (Pa)', fontsize=12)
pylab.title('Shear Stress')
plt.show()
def plot_tilt_freq(axis, tiltfft, axis_ev, label=None, ori='V', if_log=0):
tiltfft = tiltfft / tiltfft.max()
tiltfft = tiltfft + 1e-30
if ori == 'V':
alabel = 'y'
else:
alabel = 'x'
title = 'spatial spectrum at ' + label
if if_log == 1:
tiltfft = np.log(tiltfft)
title = title + ', log'
plt.imshow(tiltfft,
cmap='jet',
extent=[
axis_ev.max(),
axis_ev.min(),
axis.min() * 1e6,
axis.max() * 1e6
])
plt.colorbar()
if if_log == 1:
cmin = np.max(tiltfft) - 10
plt.clim(cmin)
plt.axis('tight')
plt.title(title, fontsize=18)
plt.xlabel('eV', fontsize=18)
plt.ylabel(alabel + r'($\mu$m)', fontsize=18)
def plot_modes_6(filename,modes=[0,1,2,3,49,499]):
assert(len(modes)==6)
X = numpy.load(filename)
evecs = X['evecs']
print evecs.shape
N = int( numpy.sqrt(evecs.shape[0]) )
pylab.figure(figsize=(7,10))
for i in range(6):
evec = evecs[:,modes[i]]
pylab.subplot(321+i)
pylab.imshow(evec.reshape((N,N)),
origin='lower',
cmap=pylab.cm.RdGy,
extent=(0,60,0,60) )
pylab.title('n=%i' % (modes[i]+1))
pylab.colorbar()
cmax = numpy.max(abs(evec))
pylab.clim(-cmax,cmax)
pylab.xlabel('arcmin')
if i%2 == 1:
pylab.gca().yaxis.set_major_formatter(NullFormatter())
else:
pylab.ylabel('arcmin')
def saveslices3d(img, clim, title):
'''Saves a single PNG image of 3D image array img, equalizes colormap with
list object clim and saves it under filename title.'''
nslices = img.shape[2]
axis = np.ceil(math.sqrt(nslices))
fig = pl.figure(num=None, figsize=(10, 6), dpi=80)
fig.suptitle(title, fontsize=14)
for n in range(nslices):
if axis != 1:
pl.subplot(axis - 1,
axis + 1,
n + 1,
frameon=False,
xticks=[],
yticks=[])
else:
pl.subplot(1, 1, n + 1, frameon=False, xticks=[], yticks=[])
pl.imshow(rotateslice(img[:, :, n]), interpolation='nearest')
stitle = "Slice %r" % (n + 1)
pl.clim(clim)
cb = pl.colorbar(shrink=.75,
ticks=range(0, int(round(clim[1])),
int(round(clim[1])) / 4))
for t in cb.ax.get_yticklabels():
t.set_fontsize(8)
pl.xlabel(stitle, fontsize=8)
pl.tight_layout()
pl.savefig(title + '.png', bbox_inches=0)
def plotConn():
# Create plot
figh = figure(figsize=(8,6))
figh.subplots_adjust(left=0.02) # Less space on left
figh.subplots_adjust(right=0.98) # Less space on right
figh.subplots_adjust(top=0.96) # Less space on bottom
figh.subplots_adjust(bottom=0.02) # Less space on bottom
figh.subplots_adjust(wspace=0) # More space between
figh.subplots_adjust(hspace=0) # More space between
h = axes()
totalconns = zeros(shape(f.connprobs))
for c1 in range(size(f.connprobs,0)):
for c2 in range(size(f.connprobs,1)):
for w in range(f.nreceptors):
totalconns[c1,c2] += f.connprobs[c1,c2]*f.connweights[c1,c2,w]*(-1 if w>=2 else 1)
imshow(totalconns,interpolation='nearest',cmap=bicolormap(gap=0))
# Plot grid lines
hold(True)
for pop in range(f.npops):
plot(array([0,f.npops])-0.5,array([pop,pop])-0.5,'-',c=(0.7,0.7,0.7))
plot(array([pop,pop])-0.5,array([0,f.npops])-0.5,'-',c=(0.7,0.7,0.7))
# Make pretty
h.set_xticks(range(f.npops))
h.set_yticks(range(f.npops))
h.set_xticklabels(f.popnames)
h.set_yticklabels(f.popnames)
h.xaxis.set_ticks_position('top')
xlim(-0.5,f.npops-0.5)
ylim(f.npops-0.5,-0.5)
clim(-abs(totalconns).max(),abs(totalconns).max())
colorbar()
def plotWeightChanges():
if f.usestdp:
# create plot
figh = figure(figsize=(1.2*8,1.2*6))
figh.subplots_adjust(left=0.02) # Less space on left
figh.subplots_adjust(right=0.98) # Less space on right
figh.subplots_adjust(top=0.96) # Less space on bottom
figh.subplots_adjust(bottom=0.02) # Less space on bottom
figh.subplots_adjust(wspace=0) # More space between
figh.subplots_adjust(hspace=0) # More space between
h = axes()
# create data matrix
wcs = [x[-1][-1] for x in f.allweightchanges] # absolute final weight
wcs = [x[-1][-1]-x[0][-1] for x in f.allweightchanges] # absolute weight change
pre,post,recep = zip(*[(x[0],x[1],x[2]) for x in f.allstdpconndata])
ncells = int(max(max(pre),max(post))+1)
wcmat = zeros([ncells, ncells])
for iwc,ipre,ipost,irecep in zip(wcs,pre,post,recep):
wcmat[int(ipre),int(ipost)] = iwc *(-1 if irecep>=2 else 1)
# plot
imshow(wcmat,interpolation='nearest',cmap=bicolormap(gap=0,mingreen=0.2,redbluemix=0.1,epsilon=0.01))
xlabel('post-synaptic cell id')
ylabel('pre-synaptic cell id')
h.set_xticks(f.popGidStart)
h.set_yticks(f.popGidStart)
h.set_xticklabels(f.popnames)
h.set_yticklabels(f.popnames)
h.xaxif.set_ticks_position('top')
xlim(-0.5,ncells-0.5)
ylim(ncells-0.5,-0.5)
clim(-abs(wcmat).max(),abs(wcmat).max())
colorbar()
def plot_discharge(self,grid):
"""This plots discharge across the raster"""
grid._setup_active_inlink_and_outlink_matrices()
outlink = grid.node_active_outlink_matrix
inlink = grid.node_active_inlink_matrix
outlink1 = outlink.tolist()
inlink1 =inlink.tolist()
newin0, newout0 = change_signs(inlink1[0], outlink1[0])
newin1, newout1 = change_signs(inlink1[1], outlink1[1])
in0 = np.array(newin0)
in1 = np.array(newin1)
out0 = np.array(newout0)
out1 = np.array(newout1)
self.q_node = self.q[in0]+self.q[in1]+self.q[out0]+self.q[out1] #((q[outlink[1]] -q[inlink[1]]))#+((q[outlink[0]]-(q[inlink[0]])))
fixed_q = grid.zeros(centering='node')
for each in self.interior_nodes:
fixed_q[each] = self.q_node[each]
plt.figure('DISCHARGE')
hr = grid.node_vector_to_raster(fixed_q)
palette = pylab.cm.RdYlBu
im2 = pylab.imshow(hr, cmap=palette, extent=[0, grid.number_of_node_columns *grid.dx,0, grid.number_of_node_rows * grid.dx])
pylab.clim(vmin=0.000001)#, mx)
palette.set_under('w', 0.000001)
cb = pylab.colorbar(im2)
cb.set_label('DISCHARGE (m)', fontsize=12)
pylab.title('DISCHARGE')
plt.show()
def plot_tilt(axis, tilt, axis_t, label=None, ori='V', if_log=0):
tilt = tilt / tilt.max()
tilt = tilt + 1e-30
if ori == 'V':
alabel = 'y'
else:
alabel = 'x'
title = 'wavefront tilt at ' + label
if if_log == 1:
tilt = np.log(tilt)
title = title + ', log'
plt.imshow(tilt,
cmap='jet',
extent=[
axis_t.max() * 1e15,
axis_t.min() * 1e15,
axis.max() * 1e6,
axis.min() * 1e6
])
plt.colorbar()
if if_log == 1:
cmin = np.max(tilt) - 10
plt.clim(cmin)
plt.axis('tight')
plt.title(title, fontsize=18)
plt.xlabel('time (fs)', fontsize=18)
plt.ylabel(alabel + r' ($\mu$m)', fontsize=18)
def transect(x, y, z, x0, y0, x1, y1, plots=0):
#convert coord to pixel coord
d0 = sqrt((x - x0)**2 + (y - y0)**2)
i0 = d0.argmin()
x0, y0 = unravel_index(i0, x.shape)
#overwrite x0,y0
d1 = plt.np.sqrt((x - x1)**2 + (y - y1)**2)
i1 = d1.argmin()
x1, y1 = unravel_index(i1, x.shape)
#overwrite x1,y1
#-- Extract the line...
# Make a line with "num" points...
length = int(plt.np.hypot(x1 - x0, y1 - y0))
xi, yi = plt.np.linspace(x0, x1, length), plt.np.linspace(y0, y1, length)
# Extract the values along the line
#y is the first dimension and x is the second, row,col
zi = z[xi.astype(plt.np.int), yi.astype(plt.np.int)]
mz = nonaninf(z.ravel()).mean()
sz = nonaninf(z.ravel()).std()
if plots == 1:
plt.matshow(z)
plt.clim([mz - 2 * sz, mz + 2 * sz])
plt.colorbar()
plt.title('transect: (' + str(x0) + ',' + str(y0) + ') (' + str(x1) +
',' + str(y1) + ')')
plt.scatter(yi, xi, 5, c='r', edgecolors='none')
plt.figure()
plt.scatter(sqrt((xi - xi[0])**2 + (yi - yi[0])**2), zi)
#plt.figure();plt.scatter(xi, zi)
#plt.figure();plt.scatter(yi, zi)
return (xi, yi, zi)
def three_component_plot(c1, c2, c3, color, labels):
pl.figure(figsize=(8,8))
kwargs = dict(s=4, lw=0, c=color, vmin=2, vmax=6)
ax1 = pl.subplot(221)
pl.scatter(c1, c2, **kwargs)
pl.ylabel('component 2')
ax2 = pl.subplot(223, sharex=ax1)
pl.scatter(c1, c3, **kwargs)
pl.xlabel('component 1')
pl.ylabel('component 3')
ax3 = pl.subplot(224, sharey=ax2)
pl.scatter(c2, c3, **kwargs)
pl.xlabel('component 2')
for ax in (ax1, ax2, ax3):
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
pl.subplots_adjust(hspace=0.05, wspace=0.05)
format = ticker.FuncFormatter(lambda i, *args: labels[i])
pl.colorbar(ticks = range(2, 7), format=format,
cax = pl.axes((0.52, 0.51, 0.02, 0.39)))
pl.clim(1.5, 6.5)
def plot_eigenmodes(evecs, evals, ax_list, mode_list, RArange, DECrange):
"""
Plot KL eigenmodes associated with the COSMOS catalog
"""
assert len(ax_list) == len(mode_list)
NRA = len(RArange) - 1
NDEC = len(DECrange) - 1
for i in range(len(ax_list)):
ax = ax_list[i]
mode = mode_list[i]
pylab.axes(ax)
evec = evecs[:, i]
pylab.imshow(
evec.reshape((NRA, NDEC)).T,
origin="lower",
interpolation=None, #'nearest',
cmap=pylab.cm.RdGy,
extent=(RArange[0], RArange[-1], DECrange[0], DECrange[-1]),
)
cmax = np.max(abs(evec))
pylab.clim(-cmax, cmax)
pylab.title(r"$\mathrm{mode\ %i}\ (v=%.3f)$" % (i + 1, evals[i]))
return ax_list
def main(argv):
filename = argv[1]
print filename
data = np.loadtxt(filename)
pl.imshow(data > 0.1)
pl.title('natural')
pl.figure()
pl.imshow(data)
pl.clim([-0.3, 0.3])
pl.title('natural')
pl.figure()
pl.plot(data[50, :], 'b')
pl.plot(data[:, 50], 'g')
pl.ylim([-0.4, 0.4])
pl.title('natural')
pl.savefig(filename + ".linesamples.eps")
pl.imsave(filename + ".gt0p1.png", data > 0.1)
pl.imsave(filename + ".ltneg0p1.png", data < -0.1)
pl.imsave(filename + ".data.png", data, vmin=-0.3, vmax=0.3)
pl.show()
exit()
def transect(x,y,z,x0,y0,x1,y1,plots=0):
#convert coord to pixel coord
d0=sqrt( (x-x0)**2+ (y-y0)**2 );
i0=d0.argmin();
x0,y0=unravel_index(i0,x.shape); #overwrite x0,y0
d1=plt.np.sqrt( (x-x1)**2+ (y-y1)**2 );
i1=d1.argmin();
x1,y1=unravel_index(i1,x.shape); #overwrite x1,y1
#-- Extract the line...
# Make a line with "num" points...
length = int(plt.np.hypot(x1-x0, y1-y0))
xi, yi = plt.np.linspace(x0, x1, length), plt.np.linspace(y0, y1, length)
# Extract the values along the line
#y is the first dimension and x is the second, row,col
zi = z[xi.astype(plt.np.int), yi.astype(plt.np.int)]
mz=nonaninf(z.ravel()).mean()
sz=nonaninf(z.ravel()).std()
if plots==1:
plt.matshow(z);plt.clim([mz-2*sz,mz+2*sz]);plt.colorbar();plt.title('transect: (' + str(x0) + ',' + str(y0) + ') (' +str(x1) + ',' +str(y1) + ')' );
plt.scatter(yi,xi,5,c='r',edgecolors='none')
plt.figure();plt.scatter(sqrt( (xi-xi[0])**2 + (yi-yi[0])**2 ) , zi)
#plt.figure();plt.scatter(xi, zi)
#plt.figure();plt.scatter(yi, zi)
return (xi, yi, zi);
def scatter_2D(x,
y,
t=None,
cmap=pylab.cm.jet,
clim=None,
xlabel=None,
ylabel=None,
title=""):
fig = pylab.figure()
if t == None:
cb = pylab.scatter(x, y, s=12.0, linewidths=0)
else:
cb = pylab.scatter(x, y, c=t, cmap=cmap, s=12.0, linewidths=0)
if xlabel == None:
xlabel = 'x'
if ylabel == None:
ylabel = 'y'
pylab.xlabel(xlabel)
pylab.ylabel(ylabel)
pylab.title(title)
pylab.colorbar(cb)
if clim != None:
pylab.clim(clim[0], clim[1])
return fig
def show_kappa(kappa,clabel=r'\kappa',part='r',clim=None,logplot=True):
if part.lower()=='r':
kappa = kappa.real
elif part.lower()=='i':
kappa = kappa.imag
elif part.lower() in ['a','n']:
kappa = abs(kappa)
else:
raise ValueError, "show_kappa : unrecognized part %s" % part
pylab.figure(figsize=(14,9))
if logplot:
kappa = numpy.log(1+kappa)
pylab.imshow(kappa.T,
origin = 'lower',
interpolation = 'nearest',
extent = params.RAlim+params.DEClim)
cb = pylab.colorbar()
if logplot:
cb.set_label(r'$\rm{log}(1+%s)$' % clabel,
fontsize=14)
else:
cb.set_label(r'$%s$' % clabel,
fontsize=14)
if clim is not None:
pylab.clim(clim)
pylab.xlabel('RA (deg)')
pylab.ylabel('DEC (deg)')
def plot_discharge(self, grid):
"""This plots discharge across the raster"""
grid._setup_active_inlink_and_outlink_matrices()
outlink = grid.node_active_outlink_matrix
inlink = grid.node_active_inlink_matrix
outlink1 = outlink.tolist()
inlink1 = inlink.tolist()
newin0, newout0 = change_signs(inlink1[0], outlink1[0])
newin1, newout1 = change_signs(inlink1[1], outlink1[1])
in0 = np.array(newin0)
in1 = np.array(newin1)
out0 = np.array(newout0)
out1 = np.array(newout1)
self.q_node = self.q[in0] + self.q[in1] + self.q[out0] + self.q[
out1] #((q[outlink[1]] -q[inlink[1]]))#+((q[outlink[0]]-(q[inlink[0]])))
fixed_q = grid.zeros(centering='node')
for each in self.interior_nodes:
fixed_q[each] = self.q_node[each]
plt.figure('DISCHARGE')
hr = grid.node_vector_to_raster(fixed_q)
palette = pylab.cm.RdYlBu
im2 = pylab.imshow(hr,
cmap=palette,
extent=[
0, grid.number_of_node_columns * grid.dx, 0,
grid.number_of_node_rows * grid.dx
])
pylab.clim(vmin=0.000001) #, mx)
palette.set_under('w', 0.000001)
cb = pylab.colorbar(im2)
cb.set_label('DISCHARGE (m)', fontsize=12)
pylab.title('DISCHARGE')
plt.show()
def plot_mtx(mtx, labels, title):
pl.figure()
pl.imshow(mtx, interpolation='nearest')
pl.xticks(range(len(mtx)), labels, rotation=-45)
pl.yticks(range(len(mtx)), labels)
pl.title(title)
pl.clim((0,1))
pl.colorbar()
def rsa_result_plot(mtx, labels, title):
pl.figure()
pl.imshow(mtx, interpolation='nearest')
pl.xticks(range(len(mtx)), labels, rotation=-45)
pl.yticks(range(len(mtx)), labels)
pl.title(title)
pl.clim((0, 2))
pl.colorbar()
def plot_mtx(mtx, labels, title):
pl.figure()
pl.imshow(mtx, interpolation='nearest')
pl.xticks(range(len(mtx)), labels, rotation=-90)
pl.yticks(range(len(mtx)), labels)
pl.title(title)
pl.clim((0, 1))
pl.colorbar()
def visualise_stamp(self,
i,
bands=['r', 'i'],
batch=0,
cmap='jet',
nstamp=0):
psf = []
gal = []
mgal = []
for band in bands:
path1 = '%s/%c/galaxies-%d/' % (self.paths['cutouts'], band, batch)
path1 = glob.glob(path1 + '%d-cutout-HSC-%c*.fits' %
(i, band.upper()))
path1 = path1[0]
ppath1 = '%s/%c/psf-%d/' % (self.paths['cutouts'], band, batch)
ppath1 = glob.glob(ppath1 + '%d-psf-calexp*-HSC-%c*.fits' %
(i, band.upper()))
ppath1 = ppath1[0]
mpath1 = '%s/%c/galaxies-%d/' % (self.paths['cutouts'], band,
batch)
mpath1 = glob.glob(mpath1 + '%d-*masked*-HSC-%c*.fits' %
(i, band.upper()))
mpath1 = mpath1[0]
psf.append(fi.FITS(ppath1)[-1].read())
gal.append(fi.FITS(path1)['IMAGE'].read())
mgal.append(fi.FITS(mpath1)[-1].read())
j = 1
N = len(psf)
gmax = gal[0].max()
gmin = gal[0].min()
mgmax = mgal[0].max()
mgmin = mgal[0].min()
for i, (p, g, masked) in enumerate(zip(psf, gal, mgal)):
if nstamp > 0:
x0 = g.shape[1] / 2
y0 = g.shape[0] / 2
dx = nstamp / 2
dy = nstamp / 2
g = g[y0 - dy:y0 + dy, x0 - dx:x0 + dx]
masked = masked[y0 - dy:y0 + dy, x0 - dx:x0 + dx]
plt.subplot(int('%d%d%d' % (N, 3, j)))
plt.imshow(g, interpolation='none', cmap=cmap)
plt.clim(mgmin, mgmax)
print mgmin, mgmax
j += 1
plt.subplot(int('%d%d%d' % (N, 3, j)))
plt.imshow(masked, interpolation='none', cmap=cmap)
plt.clim(mgmin, mgmax)
j += 1
plt.subplot(int('%d%d%d' % (N, 3, j)))
plt.imshow(p, interpolation='none', cmap=cmap)
j += 1
def plot_spectrogram(input_data,
windowfn=None,
channel_number=0,
filename=None,
coloraxis=None,
noverlap=0,
NFFT=None,
**kwargs):
import pylab as pl
if windowfn == None: windowfn = pl.window_hanning
# look in the config file section Plots for NFFT = 1234
# Dave - how about a method to allow this in one line
# e.g. pyfusion.config.numgetdef('Plots','NFFT', 2048)
# usage:
# if (NFFT==None): NFFT = pyfusion.config.numgetdef('Plots','NFFT', 2048)
#
# also nice to have pyfusion.config.re-read()
if NFFT == None:
try:
NFFT = (eval(pyfusion.config.get('Plots', 'NFFT')))
except:
NFFT = 2048
pl.specgram(input_data.signal.get_channel(channel_number),
NFFT=NFFT,
noverlap=noverlap,
Fs=input_data.timebase.sample_freq,
window=windowfn,
**kwargs)
#accept multi or single channel data (I think?)
if coloraxis != None: pl.clim(coloraxis)
else:
try:
pl.clim(eval(pyfusion.config.get('Plots', 'coloraxis')))
except:
pass
# look in the config file section Plots for a string like FT_Axis = [0,0.08,0,500]
# don't quote
try:
pl.axis(eval(pyfusion.config.get('Plots', 'FT_Axis')))
except:
pass
try:
pl.title("%d, %s" % (input_data.meta['shot'],
input_data.channels[channel_number].name))
except:
pl.title("%d, %s" %
(input_data.meta['shot'], input_data.channels.name))
if filename != None:
pl.savefig(filename)
else:
pl.show()
def animate(i):
im.set_array(data[i])
if saturation is not None:
saturate(saturation, im=im)
else:
plt.clim(clims)
if label is not None:
tx.set_text('{:2.0f} {:}'.format(label[i], label_caption))
return [im]
def output_plots(image_field, axis):
arr = image_field.swapaxes(0, axis)
for i in range(arr.shape[axis]):
print i
pylab.clf()
pylab.imshow(arr[i,...], interpolation='nearest')
pylab.colorbar()
pylab.clim(image_field.min(), image_field.max())
pylab.savefig("RT/image_%s_%04i.png" % (axis,i))
def saltmovie(tr):
mv = anim.Movie()
ints = np.sort(np.unique(tr.ints))
for i in ints[:40]:
pl.pcolormesh(miv(tr.traj2grid('z',tr.jd==i)),cmap=cm.Paired)
pl.clim(28,33)
print i
mv.image()
mv.video(tr.projname+tr.casename+"_salt_mov.mp4")
def movie(self,fld,k=0,jd1=733342,jd2=733342+10):
miv = np.ma.masked_invalid
for n,jd in enumerate(np.arange(jd1,jd2,0.25)):
self.load(fld,jd)
pl.clf()
pl.pcolormesh(miv(self.__dict__[fld][0,k,:,:]))
pl.clim(-0.1,0.1)
pl.savefig('%s_%05i.png' % (fld,n),dpi=150)
def animate(i):
im.set_array(F[i].ravel())
if saturation is not None:
sp.saturate(saturation)
else:
plt.clim(clims)
if label is not None:
tx.set_text('{:2.0f} {:}'.format(label[i], label_caption))
return [im]
def animate(i):
im.set_array(data[i])
if saturation is not None:
saturate(saturation, im=im)
else:
plt.clim(clims)
if label is not None:
tx.set_text('{:2.1f} {:}'.format(label[i], label_caption))
return [im]
def pcolor(self, field, jd=None):
"""Plot a map of a field using projmap"""
self.add_mp()
pl.clf()
pl.subplot(111,axisbg='0.9')
self.mp.pcolormesh(self.mpxll,self.mpyll,miv(field), cmap=GBRY())
self.mp.nice()
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d"))
pl.clim(-10,10)
pl.colorbar(aspect=40,shrink=0.95,pad=0,fraction=0.05)
def Bsf(self):
if not hasattr(self, 'Br'):
self.Bfeild()
pl.streamplot(self.r,
self.z,
self.Br.T,
self.Bz.T,
color=self.Br.T,
cmap=pl.cm.RdBu)
pl.clim([-1.5, 1.5])
def plot_mtx(mtx, labels, title, skip=5):
# little helper function to plot dissimilarity matrices
# if using correlation-distance, we use colorbar range of [0,2]
pl.figure()
pl.imshow(mtx, interpolation='nearest')
pl.xticks(range(len(mtx))[::skip], labels[::skip], rotation=90)
pl.yticks(range(len(mtx))[::skip], labels[::skip])
pl.title(title)
pl.clim((0, 2))
pl.colorbar()
def pcolor(self, field, jd=None):
"""Plot a map of a field using projmap"""
self.add_mp()
pl.clf()
pl.subplot(111,axisbg='0.9')
self.mp.pcolormesh(self.mpxll,self.mpyll,miv(field), cmap=GBRY())
self.mp.nice()
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d"))
pl.clim(-10,10)
pl.colorbar(aspect=40,shrink=0.95,pad=0,fraction=0.05)
def interp(ps,intstart=6353):
import figpref
import projmaps
figpref.current()
ps.c.execute("SELECT distinct(ints) FROM casco__chlor_a " +
" WHERE intstart=%i" % intstart)
ints1,ints2 = ps.c.fetchall()
sql = """SELECT t1.ntrac,t1.val,t2.val,p.x,p.y,p.ints
FROM casco__chlor_a t1
INNER JOIN casco__chlor_a t2 ON t1.ntrac=t2.ntrac
INNER JOIN casco p ON t1.ntrac=p.ntrac
WHERE t1.intstart=%i
AND t2.intstart=%i
AND p.intstart=%i
AND t1.ints=%i
AND t2.ints=%i;
""" % (intstart,intstart,intstart,ints1[0],ints2[0])
ps.c.execute(sql)
res = zip(*ps.c.fetchall())
class trj: pass
if len(res) > 0:
for n,a in enumerate(['ntrac','t1val','t2val','x','y','ints']):
trj.__dict__[a] = np.array(res[n])
mask = (trj.ints.astype(np.float)/100-trj.ints/100)>0.5
trj.ints[mask]=trj.ints[mask]+1
tvec = np.unique(trj.ints)
tvec = tvec[tvec<=ints2[0]]
itvec = tvec-tvec.min()
itvec = itvec.astype(np.float)/itvec.max()
mp = projmaps.Projmap('casco')
xl,yl = mp(ps.llon,ps.llat)
for n,t in enumerate(tvec):
fld = ps.cs.llat * 0
cnt = ps.cs.llat * 0
xvc = (trj.x[trj.ints==t].astype(np.int))
yvc = (trj.y[trj.ints==t].astype(np.int))
val = ( np.log(trj.t1val[trj.ints==t])*(1-itvec[n]) +
np.log(trj.t2val[trj.ints==t])*(itvec[n]) )
for x,y,v in zip(xvc,yvc,val):
fld[y,x] += v
cnt[y,x] += 1
pl.clf()
mp.pcolormesh(xl,yl,miv(fld/cnt))
mp.nice()
jd = (ps.base_iso +
(float(t)-(intstart)/8*3600*24)/3600/24 + intstart/8) + 0.583
pl.title(pl.num2date(jd).strftime("log(Chl) %Y-%m-%d %H:%M"))
pl.clim(-2,2.5)
pl.savefig("interp_%i_%03i.png" % (intstart,n), dpi=100)
def plot_spectrogram(input_data, windowfn=None, units='kHz', channel_number=0, filename=None, coloraxis=None, noverlap=0,NFFT=None, **kwargs):
import pylab as pl
if windowfn == None: windowfn=pl.window_hanning
# look in the config file section Plots for NFFT = 1234
# Dave - how about a method to allow this in one line
# e.g. pyfusion.config.numgetdef('Plots','NFFT', 2048)
# usage:
# if (NFFT==None): NFFT = pyfusion.config.numgetdef('Plots','NFFT', 2048)
#
# also nice to have pyfusion.config.re-read()
if NFFT == None:
try:
NFFT=(int(pyfusion.config.get('Plots','NFFT')))
except:
NFFT = 2048
print(NFFT)
if units.lower() == 'khz': ffact = 1000.
else: ffact =1.
xextent=(min(input_data.timebase),max(input_data.timebase))
pl.specgram(input_data.signal.get_channel(channel_number), NFFT=NFFT, noverlap=noverlap, Fs=input_data.timebase.sample_freq/ffact, window=windowfn, xextent=xextent, **kwargs)
#accept multi or single channel data (I think?)
if coloraxis != None: pl.clim(coloraxis)
else:
try:
pl.clim(eval(pyfusion.config.get('Plots','coloraxis')))
except:
pass
# look in the config file section Plots for a string like
# FT_Axis = [0,0.08,0,500e3] don't quote
try:
#pl.axis(eval(pyfusion.config.get('Plots','FT_Axis')))
# this is clumsier now we need to consider freq units.
axt = eval(pyfusion.config.get('Plots','FT_Axis'))
pl.axis([axt[0], axt[1], axt[2]/ffact, axt[3]/ffact])
except:
pass
# but override X if we have zoomed in bdb
if 'reduce_time' in input_data.history:
pl.xlim(np.min(input_data.timebase),max(input_data.timebase))
try:
pl.title("%d, %s"%(input_data.meta['shot'], input_data.channels[channel_number].name))
except:
pl.title("%d, %s"%(input_data.meta['shot'], input_data.channels.name))
if filename != None:
pl.savefig(filename)
else:
pl.show()
def interp(ps,intstart=6353):
import figpref
import projmaps
figpref.current()
ps.c.execute("SELECT distinct(ints) FROM casco__chlor_a " +
" WHERE intstart=%i" % intstart)
ints1,ints2 = ps.c.fetchall()
sql = """SELECT t1.ntrac,t1.val,t2.val,p.x,p.y,p.ints
FROM casco__chlor_a t1
INNER JOIN casco__chlor_a t2 ON t1.ntrac=t2.ntrac
INNER JOIN casco p ON t1.ntrac=p.ntrac
WHERE t1.intstart=%i
AND t2.intstart=%i
AND p.intstart=%i
AND t1.ints=%i
AND t2.ints=%i;
""" % (intstart,intstart,intstart,ints1[0],ints2[0])
ps.c.execute(sql)
res = zip(*ps.c.fetchall())
class trj: pass
if len(res) > 0:
for n,a in enumerate(['ntrac','t1val','t2val','x','y','ints']):
trj.__dict__[a] = np.array(res[n])
mask = (trj.ints.astype(np.float)/100-trj.ints/100)>0.5
trj.ints[mask]=trj.ints[mask]+1
tvec = np.unique(trj.ints)
tvec = tvec[tvec<=ints2[0]]
itvec = tvec-tvec.min()
itvec = itvec.astype(np.float)/itvec.max()
mp = projmaps.Projmap('casco')
xl,yl = mp(ps.llon,ps.llat)
for n,t in enumerate(tvec):
fld = ps.cs.llat * 0
cnt = ps.cs.llat * 0
xvc = (trj.x[trj.ints==t].astype(np.int))
yvc = (trj.y[trj.ints==t].astype(np.int))
val = ( np.log(trj.t1val[trj.ints==t])*(1-itvec[n]) +
np.log(trj.t2val[trj.ints==t])*(itvec[n]) )
for x,y,v in zip(xvc,yvc,val):
fld[y,x] += v
cnt[y,x] += 1
pl.clf()
mp.pcolormesh(xl,yl,miv(fld/cnt))
mp.nice()
jd = (ps.base_iso +
(float(t)-(intstart)/8*3600*24)/3600/24 + intstart/8) + 0.583
pl.title(pl.num2date(jd).strftime("log(Chl) %Y-%m-%d %H:%M"))
pl.clim(-2,2.5)
pl.savefig("interp_%i_%03i.png" % (intstart,n), dpi=100)
def plot_shear_stress_grid(self, grid):
'''
This method must follow a call to the flow_across_grid() method
and requires the instance of the raster grid which was created
when the DEM is read in.
Shear stress is calculated at each interior node, to correspond with
where water depths are calculated (interior nodes).
This takes that node vector, converts it into a raster.
Presently, all nodes the colorbar is blocked at intervals of ten, using
the following function call:
>>> cmap = plt.get_cmap('Spectral', 10)
To change the interval at which color blocks are set, the value of 10 can
be changed.
To specify a particular range of shear stress values, the following call
can be added to the function. Say we want the low values on the color bar to start at 10
and increase to 100, which is currently the default setting:
>>> pylab.clim(vmin=10, vmax=100)
The colorbar is plotted using a blocked color map like those found here:
http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
The Spectral map has small values starting at shades of pink while increasing
values go over the color spectrum from orange, to yellow, to green, to blue and
purple as the values increase.
'''
# First we create a new figure window to plot within
plt.figure('Shear Stress raster')
# And then we convert the shear stress node vector to raster format
tn = grid.node_vector_to_raster(self.tau)
# This call selects the color map and interval which it will be blocked by
cmap=plt.get_cmap('Spectral', 10)
# This sets the minimum on the color bar, anything beneath the lower clim() value is set to white.
cmap.set_under('white')
# Creating the grid image to show
im2 = pylab.imshow(tn, cmap=cmap, extent=[0, grid.number_of_node_columns *grid.dx,0, grid.number_of_node_rows * grid.dx])
# Creating the colorbar for the grid image
cb = pylab.colorbar(im2)
# Sets the minimum and maximum values for the colorbar.
pylab.clim(vmin=10, vmax=100)
# Set titles for the figure and colorbar.
cb.set_label('Shear Stress (Pa)', fontsize=12)
pylab.title('Shear Stress')
plt.show()
def plot_water_depths(self, grid):
'''
This method must follow a call to either flow_at_one_node() or the
flow_across_grid() methods and requires the instance of the raster
grid which was created when the DEM is read in.
Water depths are calculated at each interior node in both methods.
This takes that node vector, converts it into a raster.
Presently, all nodes with water depths less than 1 cm are considered to have
no flow/no depth and are plotted as white in the raster, using the following call:
>>> palette.set_under('w', 0.01)
If this value is changed, the value in the pylab.clim()
function MUST also be changed.
Additionally, the maximum value for plotted water depths is 1 m,
using the following call:
>>> pylab.clim(0.01, 1)
Changing the value of 1 will extend the colorbar to higher values.
Depths are plotted using a continuous color map, like those
found here: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
The winter_r is simply a reverse of the winter colormap, so small values
are shaded green while increasing values are shaded with increasingly dark
shades of blue.
'''
plt.figure('Water Depth Raster')
# This function takes the node vector and converts to a raster.
hr = grid.node_vector_to_raster(self.h)
# Setting color selections - this is a green to blue continuous colorbar
palette = pylab.cm.winter_r
# Anything beneath the float value in this argument is colored white.
palette.set_under('w', 0.01) ## Hardcoded 1 cm min. depth.
# Creates the grid image to show.
im2 = pylab.imshow(hr, cmap=palette, extent=[0, grid.number_of_node_columns *grid.dx,0, grid.number_of_node_rows * grid.dx])
# Sets the minimum and maximum values for the colorbar.
pylab.clim(0.01, 1) # The left value or "min" must be equal to the value in the set_under() function call above. Currently
# hardcoded to 1 m.
# Creates the colorbar
cb = pylab.colorbar(im2)
# Labels for the colorbar and figure.
cb.set_label('Water depth (m)', fontsize=12)
pylab.title('Water depth')
plt.show()
def plot_water_depths(self, grid):
'''
This method must follow a call to either flow_at_one_node() or the
flow_across_grid() methods and requires the instance of the raster
grid which was created when the DEM is read in.
Water depths are calculated at each interior node in both methods.
This takes that node vector, converts it into a raster.
Presently, all nodes with water depths less than 1 cm are considered to have
no flow/no depth and are plotted as white in the raster, using the following call:
>>> palette.set_under('w', 0.01) # doctest: +SKIP
If this value is changed, the value in the pylab.clim()
function MUST also be changed.
Additionally, the maximum value for plotted water depths is 1 m,
using the following call:
>>> pylab.clim(0.01, 1) # doctest: +SKIP
Changing the value of 1 will extend the colorbar to higher values.
Depths are plotted using a continuous color map, like those
found here: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
The winter_r is simply a reverse of the winter colormap, so small values
are shaded green while increasing values are shaded with increasingly dark
shades of blue.
'''
plt.figure('Water Depth Raster')
# This function takes the node vector and converts to a raster.
hr = grid.node_vector_to_raster(self.h)
# Setting color selections - this is a green to blue continuous colorbar
palette = pylab.cm.winter_r
# Anything beneath the float value in this argument is colored white.
palette.set_under('w', 0.01) ## Hardcoded 1 cm min. depth.
# Creates the grid image to show.
im2 = pylab.imshow(hr, cmap=palette, extent=[0, grid.number_of_node_columns *grid.dx,0, grid.number_of_node_rows * grid.dx])
# Sets the minimum and maximum values for the colorbar.
pylab.clim(0.01, 1) # The left value or "min" must be equal to the value in the set_under() function call above. Currently
# hardcoded to 1 m.
# Creates the colorbar
cb = pylab.colorbar(im2)
# Labels for the colorbar and figure.
cb.set_label('Water depth (m)', fontsize=12)
pylab.title('Water depth')
plt.show()
def plot_shear_stress_grid(self, grid):
plt.figure('Shear Stress raster')
tn = grid.node_vector_to_raster(self.tau)
cmap=plt.get_cmap('Spectral', 10)
cmap.set_under('white')
im2 = pylab.imshow(tn, cmap=cmap, extent=[0, grid.number_of_node_columns *grid.dx,0, grid.number_of_node_rows * grid.dx])
cb = pylab.colorbar(im2)
pylab.clim(vmin=10, vmax=100)
cb.set_label('Shear Stress (Pa)', fontsize=12)
pylab.title('Shear Stress')
plt.show()
def OnComputeOutput(self, e):
plusl = self.im1array[:, :, 0, 0]
negsl = self.im2array[:, :, 0, 0]
self.subtr = ComputePercentDiff(plusl, negsl)
#self.B0corrsubtr=CorrectB0(self.subtr, self.B0slice)
outclim = [0, 5]
figure, cb = plotGraph(self.subtr)
pl.clim(outclim)
cb.outline.set_linewidth(0.2)
cb.ax.tick_params(size=1, labelsize=2, pad=0)
self.OutputCanvas = FigureCanvas(self.window_6_pane_1, -1, figure)
def make_panel(image, color=cm.gray, zmin = None, zmax = None, pix_sz = 0.396):
imshape_arcsec = np.array(image.shape)*pix_sz
if zmin == None:
zmin = np.min(image)
if zmax == None:
zmax = np.max(image)
pl.imshow(image, cmap=color, interpolation = 'nearest', extent = (-imshape_arcsec[1]/2, imshape_arcsec[1]/2,-imshape_arcsec[1]/2, imshape_arcsec[1]/2), origin = 'lower')
pl.clim(zmin,zmax)
return zmin, zmax
def test2(self):
iqehist = hh.load(os.path.join(datadir, "V-iqe.h5"))
newsqe = interp(iqehist, newE=np.arange(-50, 50, 1.))
mask = dynamical_range_mask(newsqe, Ei=120)
# plot
if interactive:
import pylab
pylab.imshow(mask.T[::-1])
pylab.clim(0, None)
pylab.colorbar()
pylab.show()
return
def plot_water_depths(self, grid):
''' This plots water depths across the raster. '''
plt.figure('Water Depth Raster')
hr = grid.node_vector_to_raster(self.h)
palette = pylab.cm.winter_r
palette.set_under('w', 0.01) ## right now these are hardcoded values.
im2 = pylab.imshow(hr, cmap=palette, extent=[0, grid.number_of_node_columns *grid.dx,0, grid.number_of_node_rows * grid.dx])
pylab.clim(0.01, 1) ## hardcoded - need to think of something clever.
cb = pylab.colorbar(im2)
cb.set_label('Water depth (m)', fontsize=12)
pylab.title('Water depth')
plt.show()
def read_data_doppler(filename='Off of Newton Exit 17.wav', Tp=0.25, fc=2590E6 ):
FS,Y=readwav(filename)
#constants
c = 3E8
#radar parameters
#Tp = 0.250 # (s) pulse time
n = int(Tp*FS) # number of samples per pulse
print ['Number of samples per pulse:', n]
#fc = 2590E6 # (Hz) Center frequency (connected VCO Vtune to +5)
#the input appears to be inverted
s = -1*Y[0] #Y[:,1]
del Y
#create doppler vs. time plot data set here
#for ii =1:round(size(s,1)/N)-1
nsif=int(round(s.shape[0]/n)-1)
print ['nsif:', nsif]
sif=N.zeros([nsif,n])
for ii in xrange(0,nsif):
sif[ii,:] = s[ii*n:(ii+1)*n]
#sif(ii,:) = s(1+(ii-1)*N:ii*N)
#subtract the average DC term here
sif = sif-s.mean()
zpad = 8*n/2
#doppler vs. time plot:
v=dbv(N.fft.ifft(sif, zpad,1))
v=v[:,0:v.shape[1]/2]
mmax = v.max()
#calculate velocity
delta_f = N.linspace(0, FS, v.shape[1]) #(Hz)
wavelen=c/fc
velocity = delta_f*wavelen/2.
#calculate time
time = N.linspace(0,Tp*v.shape[0],v.shape[0]) #(sec)
#plot
P.pcolormesh(velocity[0:512], time, v[:,0:512]-mmax, edgecolors = 'None')
P.plt.gca().invert_yaxis()
P.matshow(v[:,0:1024], extent=(velocity[0], velocity[1023], time[-1], time[0]))
P.colorbar()
P.clim([-40,0])
P.xlabel('Velocity (m/sec)')
P.ylabel('time (sec)')
P.show();
return v,velocity,time