def plot_cmc(cmc, ni=200):
import matplotlib.pyplot as plt
import pylab as P
import numpy as np
fig = P.figure()
fig.suptitle('Acumulative Match Characteristic', fontsize=18, fontweight='bold')
P.ylabel('%', fontsize=16)
P.xlabel('Rank', fontsize=16)
P.xlim(0, ni)
P.ylim(0,101)
P.xticks(np.arange(0, ni, 10.0))
P.yticks(np.arange(0, 101, 5.0))
xticklabels = P.getp(P.gca(), 'xticklabels')
yticklabels = P.getp(P.gca(), 'yticklabels')
P.setp(yticklabels, 'color', 'k', fontsize='x-large')
P.setp(xticklabels, 'color', 'k', fontsize='x-large')
P.grid(True)
fig.set_size_inches(19,7)
#P.plot(cmc*100)
P.plot(cmc*100)
fig.savefig('cmc_bf_knn.png')
P.show()
def get_chart(data, step):
fig = pylab.figure(
figsize=[1.5, 1], # Inches
dpi=100, # 100 dots per inch, so the resulting buffer is 400x400 pixels
)
ax = fig.gca()
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), visible=False)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), visible=False)
pylab.setp(pylab.getp(pylab.gca(), 'xgridlines'), 'linestyle', ' ')
pylab.setp(pylab.getp(pylab.gca(), 'ygridlines'), 'linestyle', ' ')
ax.plot(data)
ax.grid(True)
targets = dict(left=2, right=2, top=2, bottom=2, hspace=0, wspace=0)
fig.canvas.mpl_connect('resize_event',
lambda e: adjust_borders(fig, targets))
adjust_borders(fig, targets)
ax.plot([step], [data[step]], 'ro')
canvas = agg.FigureCanvasAgg(fig)
canvas.draw()
renderer = canvas.get_renderer()
raw_data = renderer.tostring_rgb()
return pygame.image.fromstring(raw_data, canvas.get_width_height(), "RGB")
def plot_cmc(cmc, metodo, fl, nf=50, dx=300, w=10, h=7):
import matplotlib.pyplot as plt
import pylab as P
import numpy as np
fig = P.figure()
fig.suptitle('Cumulative Match Characteristic',
fontsize=18,
fontweight='bold')
P.title(metodo)
P.ylabel('%', fontsize=16)
P.xlabel('Rank', fontsize=16)
P.xlim(0, nf)
P.ylim(0, 101)
P.xticks(np.arange(0, nf, dx))
P.yticks(np.arange(0, 101, 5.0))
xticklabels = P.getp(P.gca(), 'xticklabels')
yticklabels = P.getp(P.gca(), 'yticklabels')
P.setp(yticklabels, 'color', 'k', fontsize='x-large')
P.setp(xticklabels, 'color', 'k', fontsize='x-large')
P.grid(True)
fig.set_size_inches(w, h)
#P.plot(cmc*100)
P.plot(cmc * 100)
fig.savefig(fl)
P.show()
def build_cmc(arquivos, mylabels, mystyle, linestyles, metodos):
#import matplotlib.pyplot as plt
import pylab as P
import numpy as np
import matplotlib
from datetime import datetime
matplotlib.rcParams.update({'font.size': 17})
fig = P.figure()
fig.suptitle('Cumulative Matching Characteristic',
fontsize=18,
fontweight='bold')
#pos = metodos[0].find('0')
#P.title(metodos[0][11:(pos-1)])
P.ylabel('%', fontsize=16)
P.xlabel('Rank', fontsize=16)
ni = 50 #300
P.xlim(0, ni)
P.ylim(0, 101)
P.xticks(np.arange(0, ni, 10.0))
P.yticks(np.arange(0, 101, 5.0))
xticklabels = P.getp(P.gca(), 'xticklabels')
yticklabels = P.getp(P.gca(), 'yticklabels')
P.setp(yticklabels, 'color', 'k', fontsize='x-large')
P.setp(xticklabels, 'color', 'k', fontsize='x-large')
P.grid(True)
fig.set_size_inches(10, 7)
for (aq, pt, ls) in zip(arquivos, mystyle, linestyles):
#print aq
#cmc = np.loadtxt('./BOV_' + aq + '_ASIFT/cmc_bov_' + aq + '_asift.out',delimiter=',')
#cmc = np.loadtxt('./FV_' + aq + 'GMM_SIFT/cmc_fv_' + aq + '_sift.out',delimiter=',')
cmc = np.loadtxt(aq[0] + aq[1], delimiter=',')
P.plot(cmc * 100, pt, label=aq, linewidth=1, ms=10,
markevery=5) #, linestyle=ls) #[:-6])
P.legend(bbox_to_anchor=(0.95, 0.5), borderaxespad=0.0, labels=mylabels)
#fig.savefig('cmc_SIFT_ASIFT_MHSLCD.png')
DT = (datetime.now()).strftime("%Y%m%d%H%M%S")
fig.set_size_inches(12, 8)
pos = mylabels[0].find('0')
if pos <= 0:
pos = len(metodos[0]) + 1
fig.savefig('cmc_Mescla_' + DT + '.png', dpi=300, bbox_inches='tight')
#fig.savefig('cmc_SIFT_ASIFT_MHSLCD.png', dpi=300, bbox_inches='tight')
#fig.savefig('cmc_FV_SIFT.png', dpi=300, bbox_inches='tight')
#fig.savefig('cmc_BOV_ASIFT.png', dpi=300, bbox_inches='tight')
P.show()
def SetPlot(): ############################ set plot defaults
yticklabels = getp(gca(),'yticklabels')
xticklabels = getp(gca(),'xticklabels')
setp(xticklabels,fontsize='20')
setp(yticklabels,fontsize='20')
font = FontProperties( size="smaller" )
legend(numpoints=1,markerscale=.8,loc=(1.01,0.0),ncol=1,prop=font ,fancybox=True)
leg = gca().get_legend()
ltext = leg.get_texts()
setp(ltext,fontsize=10)
def enlarge_allticklines(axx, factor=2):
'''modify all tick lines in place'''
lines = []
for tick in axx.minorTicks:
xt = pylab.getp(tick.tick1line, 'ms')
pylab.setp(tick.tick2line, ms=xt * factor)
xt = pylab.getp(tick.tick2line, 'ms')
pylab.setp(tick.tick1line, ms=xt * factor)
for tick in axx.majorTicks:
xt = pylab.getp(tick.tick1line, 'ms')
pylab.setp(tick.tick2line, ms=xt * factor)
xt = pylab.getp(tick.tick2line, 'ms')
pylab.setp(tick.tick1line, ms=xt * factor)
def decoratePlot(legsize=16,xsize=16,ysize=16):
"""
Decorating the plots, text-sizes
"""
if legsize!=0:
leg=pylab.gca().get_legend()
txt=leg.get_texts()
pylab.setp(txt, fontsize=legsize,family=font)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(yticklabels, fontsize=ysize,family=font)
pylab.setp(xticklabels, fontsize=xsize,family=font)
def adjust_axis(axn=None, c='r', step=0, side='x', position=True):
'''
adjusts position and color of x or y ticks
axn = selected axis
c = new color for ticks
step = layer of offset
side = x or y
position = True or False True = top or right
'''
if axn is None:
axn = pylab.gca()
pylab.axes(axn)
#locator = ticker.AutoLocator()
#locator.autoscale()
#eval('axn.' + side + 'axis.set_major_locator(locator)')
#formatter = ticker.FixedFormatter()
#reval('axn.' + side + 'axis.set_major_formatter(formatter)')
choose_axes(axn, position=position, side=side)
yt = pylab.getp(axn, side + 'ticklabels')
ticklabels = pylab.getp(axn, side + 'ticklabels')
pylab.setp(ticklabels, 'color', c)
pylab.setp(eval('axn.' + side + 'axis.label'), color=c)
yt = pylab.getp(axn, side + 'ticklines')
pylab.setp(yt, color=c)
if step > 0:
dpad = 0
dlen = 25 * step
if side == 'x' and position:
drot = 90
ddir = 1
elif side == 'x':
drot = -90
ddir = -1
elif side == 'y' and position:
drot = 0
ddir = 1
else:
ddir = -1
drot = 180
dpad = -5
eval('axn.' + side + 'axis.get_major_ticks()')
ticklabels = pylab.getp(axn, side + 'ticklabels')
pylab.setp(ticklabels,
dashdirection=ddir,
dashlength=dlen,
dashrotation=drot,
dashpad=dpad)
eval('pylab.setp(axn.get_' + side + 'gridlines(),c=\'' + c + '\')')
pylab.draw_if_interactive()
def _draw_main_error(self):
"""
plot the error with respect to the true solution on our optional
visualization
"""
myg = self.grids[self.nlevels - 1].grid
v = self.grids[self.nlevels - 1].get_var("v")
e = v - self.true_function(myg.x2d, myg.y2d)
pylab.imshow(numpy.transpose(e[myg.ilo:myg.ihi + 1,
myg.jlo:myg.jhi + 1]),
interpolation="nearest",
origin="lower",
extent=[self.xmin, self.xmax, self.ymin, self.ymax])
pylab.xlabel("x")
pylab.ylabel("y")
pylab.title(r"current fine grid error")
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(format=formatter, shrink=0.5)
cb.ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(cb.ax, 'ymajorticklabels')
pylab.setp(cl, fontsize="small")
def _plot_topo_imshow(epochs, show_func, layout, decim,
vmin, vmax, colorbar, cmap, layout_scale):
"""Helper function: plot tfr on sensor layout"""
import pylab as pl
if cmap is None:
cmap = pl.cm.jet
ch_names = _clean_names(epochs.info['ch_names'])
pl.rcParams['axes.facecolor'] = 'k'
fig = pl.figure(facecolor='k')
pos = layout.pos.copy()
if colorbar:
pos[:, :2] *= layout_scale
pl.rcParams['axes.edgecolor'] = 'k'
sm = pl.cm.ScalarMappable(cmap=cmap,
norm=pl.normalize(vmin=vmin, vmax=vmax))
sm.set_array(np.linspace(vmin, vmax))
ax = pl.axes([0.015, 0.025, 1.05, .8], axisbg='k')
cb = fig.colorbar(sm, ax=ax)
cb_yticks = pl.getp(cb.ax.axes, 'yticklabels')
pl.setp(cb_yticks, color='w')
pl.rcParams['axes.edgecolor'] = 'w'
for idx, name in enumerate(_clean_names(layout.names)):
if name in ch_names:
ax = pl.axes(pos[idx], axisbg='k')
ch_idx = ch_names.index(name)
show_func(ax, ch_idx, 1e3 * epochs.times[0],
1e3 * epochs.times[-1], vmin, vmax)
pl.xticks([], ())
pl.yticks([], ())
# Revert global pylab config
pl.rcParams['axes.facecolor'] = 'w'
pl.rcParams['axes.edgecolor'] = 'k'
return fig
def _plot_topo_imshow(epochs, show_func, layout, decim, vmin, vmax, colorbar,
cmap, layout_scale):
"""Helper function: plot tfr on sensor layout"""
import pylab as pl
if cmap is None:
cmap = pl.cm.jet
ch_names = _clean_names(epochs.info['ch_names'])
pl.rcParams['axes.facecolor'] = 'k'
fig = pl.figure(facecolor='k')
pos = layout.pos.copy()
if colorbar:
pos[:, :2] *= layout_scale
pl.rcParams['axes.edgecolor'] = 'k'
sm = pl.cm.ScalarMappable(cmap=cmap,
norm=pl.normalize(vmin=vmin, vmax=vmax))
sm.set_array(np.linspace(vmin, vmax))
ax = pl.axes([0.015, 0.025, 1.05, .8], axisbg='k')
cb = fig.colorbar(sm, ax=ax)
cb_yticks = pl.getp(cb.ax.axes, 'yticklabels')
pl.setp(cb_yticks, color='w')
pl.rcParams['axes.edgecolor'] = 'w'
for idx, name in enumerate(_clean_names(layout.names)):
if name in ch_names:
ax = pl.axes(pos[idx], axisbg='k')
ch_idx = ch_names.index(name)
show_func(ax, ch_idx, 1e3 * epochs.times[0],
1e3 * epochs.times[-1], vmin, vmax)
pl.xticks([], ())
pl.yticks([], ())
# Revert global pylab config
pl.rcParams['axes.facecolor'] = 'w'
pl.rcParams['axes.edgecolor'] = 'k'
return fig
def plot_clusters(locations, idx, centroids):
locations = np.array(locations)
print "the cluster labels ", idx
plot(
locations[idx == 0, 0],
locations[idx == 0, 1],
"ob",
locations[idx == 1, 0],
locations[idx == 1, 1],
"or",
locations[idx == 2, 0],
locations[idx == 2, 1],
"ok",
locations[idx == 3, 0],
locations[idx == 3, 1],
"oc",
locations[idx == 4, 0],
locations[idx == 4, 1],
"oy",
locations[idx == 5, 0],
locations[idx == 5, 1],
"om",
locations[idx == 6, 0],
locations[idx == 6, 1],
"og",
)
plot(centroids[:, 0], centroids[:, 1], "sg", markersize=8)
setp(gca(), "ylim", reversed(getp(gca(), "ylim")))
show()
def _draw_solution(self):
""" plot the current solution on our optional visualization """
myg = self.grids[self.current_level].grid
v = self.grids[self.current_level].get_var("v")
pylab.imshow(
numpy.transpose(v[myg.ilo : myg.ihi + 1, myg.jlo : myg.jhi + 1]),
interpolation="nearest",
origin="lower",
extent=[self.xmin, self.xmax, self.ymin, self.ymax],
)
# pylab.xlabel("x")
pylab.ylabel("y")
if self.current_level == self.nlevels - 1:
pylab.title(r"solving $L\phi = f$")
else:
pylab.title(r"solving $Le = r$")
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(format=formatter, shrink=0.5)
cb.ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(cb.ax, "ymajorticklabels")
pylab.setp(cl, fontsize="small")
def _draw_solution(self):
""" plot the current solution on our optional visualization """
myg = self.grids[self.current_level].grid
v = self.grids[self.current_level].get_var("v")
pylab.imshow(numpy.transpose(v[myg.ilo:myg.ihi + 1,
myg.jlo:myg.jhi + 1]),
interpolation="nearest",
origin="lower",
extent=[self.xmin, self.xmax, self.ymin, self.ymax])
#pylab.xlabel("x")
pylab.ylabel("y")
if self.current_level == self.nlevels - 1:
pylab.title(r"solving $L\phi = f$")
else:
pylab.title(r"solving $Le = r$")
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(format=formatter, shrink=0.5)
cb.ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(cb.ax, 'ymajorticklabels')
pylab.setp(cl, fontsize="small")
def _draw_main_error(self):
"""
plot the error with respect to the true solution on our optional
visualization
"""
myg = self.grids[self.nlevels - 1].grid
v = self.grids[self.nlevels - 1].get_var("v")
e = v - self.true_function(myg.x2d, myg.y2d)
pylab.imshow(
numpy.transpose(e[myg.ilo : myg.ihi + 1, myg.jlo : myg.jhi + 1]),
interpolation="nearest",
origin="lower",
extent=[self.xmin, self.xmax, self.ymin, self.ymax],
)
pylab.xlabel("x")
pylab.ylabel("y")
pylab.title(r"current fine grid error")
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(format=formatter, shrink=0.5)
cb.ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(cb.ax, "ymajorticklabels")
pylab.setp(cl, fontsize="small")
def plot_size_tunning(self, xindex, yindex,independent=True):
if independent:
fig = pylab.figure()
colors=['red','blue','green','purple','orange','black','yellow']
measurment = self.data_dict[(xindex,yindex)]["ST"]
i = 0
#m = 0
for curve_label in measurment.keys():
curve = measurment[curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[key].view()[0][xindex, yindex] for key in x_values]
pylab.plot(x_values, y_values, lw=3, color=colors[i],label=curve_label)
m = max(0,max(y_values))
pylab.annotate('', xy=(measurment[curve_label]["measures"]["peak_near_facilitation"], y_values[measurment[curve_label]["measures"]["peak_near_facilitation_index"]]), xycoords='data',
xytext=(-1, 20), textcoords='offset points', arrowprops=dict(facecolor='green', shrink=0.05))
if measurment[curve_label]["measures"].has_key("peak_supression"):
pylab.annotate('', xy=(measurment[curve_label]["measures"]["peak_supression"], y_values[measurment[curve_label]["measures"]["peak_supression_index"]]), xycoords='data',
xytext=(-1, 20), textcoords='offset points', arrowprops=dict(facecolor='red', shrink=0.05))
if measurment[curve_label]["measures"].has_key("peak_far_facilitation"):
pylab.annotate('', xy=(measurment[curve_label]["measures"]["peak_far_facilitation"], y_values[measurment[curve_label]["measures"]["peak_far_facilitation_index"]]), xycoords='data',
xytext=(-1, 20), textcoords='offset points', arrowprops=dict(facecolor='blue', shrink=0.05))
i+=1
disable_top_right_axis(pylab.gca())
ax = pylab.gca()
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax.set_xticklabels(ax.get_xticks(), fontProperties)
ax.set_yticklabels(ax.get_yticks(), fontProperties)
ax.set_xlim(0,2)
ax.set_xticks([0,1,2])
m = numpy.ceil(m)
ax.set_yticks([0,m/2,m])
ax.set_ylim(0,m)
if independent:
release_fig("STC[" + str(xindex) + "," + str(yindex) + "]")
def my_2D_plot_of_arrays(xa, ya, title, xlabel, ylabel, *add_graphs):
fig = plt.figure()
ax = fig.add_subplot(111)
lines=ax.plot(xa, ya, 'b-o', markersize=2, color="green")
line = lines[0]
line.set_linewidth( 1.5 )
line.set_color( 'green' )
line.set_linestyle( '-' )
line.set_marker('s')
line.set_markerfacecolor('red')
line.set_markeredgecolor( '0.1' )
line.set_markersize( 3 )
## format the ticks
adl = mdates.AutoDateLocator()
ax.xaxis.set_major_locator(adl)
myformatter = MyAutoDateFormatter(adl)
#myformatter = mdates.AutoDateFormatter(adl)
ax.xaxis.set_major_formatter(myformatter)
ax.set_title(title)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
labels = getp(ax, 'xticklabels')
setp(labels, color='g', fontsize=9, fontname="Verdana" )
labels = getp(ax, 'yticklabels')
setp(labels, color='g', fontsize=9, fontname="Verdana" )
ax.grid(True)
fig.autofmt_xdate(bottom=0.18, rotation=60)
min_y=min(ya)
max_y=max(ya)
if (min_y<0) and (max_y >0):
ax.axhline(0, color='r', lw=4, alpha=0.5)
ax.fill_between(xa, ya, facecolor='red', alpha=0.5, interpolate=True)
#plot additional graphs
if len(add_graphs):
draw_add_2D_plot_of_arrays(add_graphs, ax)
plt.show()
def plot_stc_time_point(stc, subject, limits=[5, 10, 15], time_index=0,
surf='inflated', measure='dSPM', subjects_dir=None):
"""Plot a time instant from a SourceEstimate using matplotlib
The same could be done with mayavi using proper 3D.
Parameters
----------
stc : instance of SourceEstimate
The SourceEstimate to plot.
subject : string
The subject name (only needed if surf is a string).
time_index : int
Time index to plot.
surf : str, or instance of surfaces
Surface to use (e.g., 'inflated' or 'white'), or pre-loaded surfaces.
measure : str
The label for the colorbar. None turns the colorbar off.
subjects_dir : str, or None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
"""
subjects_dir = get_subjects_dir(subjects_dir)
pl.figure(facecolor='k', figsize=(8, 5))
hemis = ['lh', 'rh']
if isinstance(surf, str):
surf = [read_surface(op.join(subjects_dir, subject, 'surf',
'%s.%s' % (h, surf))) for h in hemis]
my_cmap = mne_analyze_colormap(limits)
for hi, h in enumerate(hemis):
coords = surf[hi][0][stc.vertno[hi]]
if hi == 0:
vals = stc_all_cluster_vis.lh_data[:, time_index]
else:
vals = stc_all_cluster_vis.rh_data[:, time_index]
ax = pl.subplot(1, 2, 1 - hi, axis_bgcolor='none')
pl.tick_params(labelbottom='off', labelleft='off')
flipper = -1 if hi == 1 else 1
sc = ax.scatter(flipper * coords[:, 1], coords[:, 2], c=vals,
vmin=-limits[2], vmax=limits[2], cmap=my_cmap,
edgecolors='none', s=5)
ax.set_aspect('equal')
pl.axis('off')
try:
pl.tight_layout(0)
except:
pass
if measure is not None:
cax = pl.axes([0.85, 0.15, 0.025, 0.15], axisbg='k')
cb = pl.colorbar(sc, cax, ticks=[-limits[2], 0, limits[2]])
cb.set_label(measure, color='w')
pl.setp(pl.getp(cb.ax, 'yticklabels'), color='w')
pl.draw()
pl.show()
def __plot(self, subplot=111, title=None, ylabel=None):
self.open_table(self.infile)
subt = self.table.query('any(ANTENNA1==%s && ANTENNA2==%s)' % (self.antid, self.antid))
datcol = subt.getcol(self.datacol)
flgcol = subt.getcol("FLAG") # only channel flag is needed to be hanled in MS
(l,m,n) = datcol.shape
if flgcol.shape != (l,m,n):
raise Exception, "Data conformation error. Shape of FLAG and %s differs." % self.datacol
nrow = subt.nrows()
subt.close()
self.close_table()
pl.ioff()
if not ylabel:
ylabel = self.datacol
for np in range(self.selnpol):
pl.ioff()
pl.figure(np+1)
pl.subplot(subplot)
pl.cla()
ip = self.selpol if self.selnpol == 1 else np
if title:
pl.title(self.infile+' Ant:'+ self.antnameused+' '+self.corrtypestr[ip])
datcol2 = datcol[ip].reshape(n)
flgcol2 = flgcol[ip].reshape(n)
if self.plotlevel > 0:
pl.ion()
#pl.plot(range(len(datcol2)), datcol2, 'g.')
pl.plot(range(len(datcol2)), numpy.ma.masked_array(datcol2, flgcol2), 'g.')
pl.xlim(0, nrow)
t1 = pl.getp(pl.gca(), 'xticklabels')
t2 = pl.getp(pl.gca(), 'yticklabels')
pl.setp((t1,t2), fontsize='smaller')
pl.ylabel(ylabel, fontsize='smaller')
pl.xlabel('row #')
pl.draw()
if self.plotlevel < 0:
outplfile=self.infile.rstrip('/')+'_scans_'+self.corrtypestr[self.selcorrtypeind[np]]+'.eps'
pl.savefig(outplfile,format='eps')
casalog.post( "Plot was written to %s" % outplfile )
def plot_histograms_of_measures(self):
histograms_lc = {}
histograms_hc = {}
for (xcoord,ycoord) in self.data_dict.keys():
for curve_type in self.data_dict[(xcoord,ycoord)].keys():
if curve_type == "ST":
curve_label = "Contrast"
else:
curve_label = "Contrastsurround"
print self.data_dict[(xcoord,ycoord)][curve_type].keys()
for measure_name in self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.high_contrast) + "%"]["measures"].keys():
if not histograms_hc.has_key(curve_type + "_" + measure_name):
histograms_hc[curve_type + "_" + measure_name]=[]
histograms_hc[curve_type + "_" + measure_name].append(self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.high_contrast) + "%"]["measures"][measure_name])
for measure_name in self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.low_contrast) + "%"]["measures"].keys():
if not histograms_lc.has_key(curve_type + "_" + measure_name):
histograms_lc[curve_type + "_" + measure_name]=[]
histograms_lc[curve_type + "_" + measure_name].append(self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.low_contrast) + "%"]["measures"][measure_name])
for key in histograms_lc.keys():
if ((len(histograms_lc[key]) != 0) and (len(histograms_hc[key]) != 0)):
fig = pylab.figure()
#pylab.title(str(numpy.mean(histograms_lc[key])) + "+/-" + str(numpy.std(histograms_lc[key])/ (len(histograms_lc[key])*len(histograms_lc[key]))) + "MeanHC: " + str(numpy.mean(histograms_hc[key])) + "+/-" + str(numpy.std(histograms_hc[key])/ (len(histograms_hc[key])*len(histograms_hc[key]))) , fontsize=12)
f = fig.add_subplot(111)
mmax = numpy.max([numpy.max(histograms_lc[key]),numpy.max(histograms_hc[key])])
mmin = numpy.min([numpy.min(histograms_lc[key]),numpy.min(histograms_hc[key])])
bins = numpy.arange(mmin-0.01,mmax+0.01,(mmax+0.01-(mmin-0.01))/10.0)
if key == 'ST_contrast_dependent_shift':
f.hist(histograms_hc[key],bins=bins,color='black',normed=False)
else:
histograms_lc[key]
histograms_hc[key]
f.hist((histograms_lc[key],histograms_hc[key]),bins=bins,color=['white','black'],normed=True,label=['Low contrast','High contrast'])
f.legend(loc = "upper left")
ax = pylab.gca()
disable_left_axis(ax)
disable_top_right_axis(ax)
remove_y_tick_labels()
ax.xaxis.set_major_locator(MaxNLocator(4))
pylab.setp(pylab.getp(ax, 'xticklabels'), fontsize=20)
#f.axvline(x=numpy.mean(histograms_lc[key]),linewidth=4, color='r')
release_fig("Histogram<" + key + ">")
print key + "LC mean :" + str(numpy.mean(histograms_lc[key]))
print key + "HC mean :" + str(numpy.mean(histograms_hc[key]))
else:
print "Histogram ", key , " empty!"
def get_chart(data, step):
fig = pylab.figure(figsize=[1.5, 1], # Inches
dpi=100, # 100 dots per inch, so the resulting buffer is 400x400 pixels
)
ax = fig.gca()
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), visible=False)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), visible=False)
pylab.setp(pylab.getp(pylab.gca(), 'xgridlines'), 'linestyle', ' ')
pylab.setp(pylab.getp(pylab.gca(), 'ygridlines'), 'linestyle', ' ')
ax.plot(data)
ax.grid(True)
targets = dict(left=2, right=2, top=2, bottom=2, hspace=0, wspace=0)
fig.canvas.mpl_connect('resize_event', lambda e: adjust_borders(fig, targets))
adjust_borders(fig, targets)
ax.plot([step], [data[step]], 'ro')
canvas = agg.FigureCanvasAgg(fig)
canvas.draw()
renderer = canvas.get_renderer()
raw_data = renderer.tostring_rgb()
return pygame.image.fromstring(raw_data, canvas.get_width_height(), "RGB")
def hist(train, test, pca=None, xlabel=None): b = _nb_bins(train, 100, 20) _, _, patches = plt.hist(train, bins=b, normed=True, color='gold', label="Train set", alpha=.5) c = plt.getp(patches[0], 'facecolor') vspans(train, c) b = _nb_bins(test, 100, 20) _, _, patches = plt.hist(test, bins=b, normed=True, color='g', label="Test set", alpha=.5) c = plt.getp(patches[0], 'facecolor') vspans(test, c) if not pca is None: b = _nb_bins(pca, 100, 20) _, _, patches = plt.hist(pca, bins=b, normed=True, color='b', label="PCA set", alpha=.5) c = plt.getp(patches[0], 'facecolor') vspans(pca, c) plt.legend(loc="best") plt.grid() plt.xlabel(xlabel)
def click_event(self, event):
"""Whenever a click occurs on the coefficent axes we modify the coefficents and update the
plot"""
if event.xdata is None: #we clicked outside the axis
return
idx = M.getp(event.inaxes, 'label')
print idx, event.xdata, event.ydata
self.c[idx] = event.xdata
self.c[idx + 1] = event.ydata
self.replotf()
self.replotc()
def click_event(self, event): """Whenever a click occurs on the coefficent axes we modify the coefficents and update the plot""" if event.xdata is None:#we clicked outside the axis return idx = M.getp(event.inaxes,'label') print idx, event.xdata, event.ydata self.c[idx] = event.xdata self.c[idx+1] = event.ydata self.replotf() self.replotc()
def plotConfusion(confusion, grid, title, file_name, inset=None, fudge=16):
pylab.figure()
axmain = pylab.axes()
tick_labels = [ "Missing", "Correct\nNegative", "False\nAlarm", "Miss", "Hit" ]
min_label = -1
xs, ys = grid.getXY()
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
tick_locs = np.linspace(-1, min_label + len(tick_labels) - 2, len(tick_labels))
tick_locs += (tick_locs[1] - tick_locs[0]) / 2
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
grid.drawPolitical()
if inset:
lb_y, lb_x = [ b.start for b in inset ]
ub_y, ub_x = [ b.stop + fudge for b in inset ]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp], ys[inset_exp], confusion[inset_exp], cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
grid.drawPolitical()
gs_x, gs_y = grid.getGridSpacing()
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
xi = +x0
for ii in xrange(Nt):
ye[:, ii] = C * xi + D * u[:, ii]
xi = A * xi + B * u[:, ii]
# plot singular values
pylab.figure(1)
nsv = 30
a = pylab.semilogy(range(1, nsv + 1), svN[:nsv] / svN[0], '-o',
range(1, nsv + 1), sv[:nsv] / sv[0], '-o')
pylab.axis([0, nsv, 1e-8, 1])
ax = pylab.gca()
pylab.setp(ax, yticks=[1e-8, 1e-6, 1e-4, 1e-2, 1e0])
pylab.xlabel('Singular value index')
pylab.ylabel('Normalized singular values')
pylab.legend(a, ('Original', 'Optimized'), loc='best')
pylab.figure(2)
for ii in range(p):
pylab.subplot(p, 1, ii + 1)
pylab.plot(t.T, y[ii, :].T, t.T, ye[ii, :].T)
pylab.hold(True)
pylab.plot([N, N], pylab.getp(pylab.gca(), 'ylim'))
pylab.hold(False)
pylab.xlabel('Time t')
pylab.ylabel('Output y' + str(ii))
pylab.legend(a, ('Measured', 'Estimated'), loc='best')
pylab.show()
def map_psf_stars(fitsfile, outfile, stars, title, vmin=10, vmax=1500):
"""
Plot combo image for each field in W51 G48.9-0.3 and overlay the PSF
stars.
"""
datadir = '/u/jlu/data/w51/'
outdir = '/u/jlu/work/w51/maps/'
##########
# Load the fits file
##########
img = pyfits.getdata(fitsfile)
##########
# Calculate the coordinates in arcsec
##########
scale = 0.00995
# Image extent
xaxis = (np.arange(img.shape[0]) - stars[0]['xpix']) * scale * -1.0
yaxis = (np.arange(img.shape[1]) - stars[0]['ypix']) * scale
for star in stars:
# RA and Dec Offsets (in arcsec)
star['x'] = (star['xpix'] - stars[0]['xpix']) * scale * -1.0
star['y'] = (star['ypix'] - stars[0]['ypix']) * scale
##########
# Plot Image
##########
py.rc('font', **{'weight':'bold'})
py.clf()
py.imshow(np.log10(img), cmap=py.cm.YlOrBr_r,
vmin=math.log10(vmin), vmax=math.log10(vmax),
extent=[xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]])
py.xlabel('R.A. Offset (arcsec)', fontweight='bold')
py.ylabel('Dec. Offset (arcsec)', fontweight='bold')
py.setp(py.getp(py.gca(), 'xticklabels'), fontweight='bold')
py.setp(py.getp(py.gca(), 'yticklabels'), fontweight='bold')
py.title(title, fontweight='bold')
fig = py.gca()
# Draw East/North arrows
arr0 = np.array([xaxis[0]-1.7, yaxis[0]+0.3])
arrLen = 1.0
arrColor = 'black'
north = py.Arrow(arr0[0], arr0[1], 0, arrLen, width=0.3*arrLen,
fill=True, fc=arrColor, ec=arrColor)
east = py.Arrow(arr0[0], arr0[1], arrLen, 0, width=0.3*arrLen,
fill=True, fc=arrColor, ec=arrColor)
fig.add_patch(north)
fig.add_patch(east)
py.text(arr0[0], arr0[1]+(1.1*arrLen), 'N', color=arrColor, weight='bold',
horizontalalignment='center', verticalalignment='bottom')
py.text(arr0[0]+(1.1*arrLen), arr0[1], 'E', color=arrColor, weight='bold',
horizontalalignment='right', verticalalignment='center')
##########
# Plot each PSF star.
##########
for star in stars:
# Encircle and Label coo star
psfRadius = 0.2
psfLabel = '%s\n[%6.3f, %6.3f]' % (star['name'], star['x'], star['y'])
psfStar = py.Circle([star['x'], star['y']],
radius=psfRadius, fill=False,
ec='black', linewidth=3)
fig.add_patch(psfStar)
py.text(star['x'], star['y']-(1.2*psfRadius), psfLabel,
fontweight='bold', color='black', fontsize=10,
horizontalalignment='center', verticalalignment='top')
# Save
py.savefig(outfile)
##########
# Generate a PSF star list for Starfinder... only print out to the screen.
##########
print '########'
print '#'
print '# PRINTING PSF starlist for input to Starfinder.'
print '# -- paste everything below to a file.'
print '# -- example file is /u/jlu/code/idl/w51/psftables/w51a_f1_psf.list'
print '#'
print '########'
print '\n'
print '# This file should contain sources we absolutely know exist '
print '# (with names). Additionally, all sources should have velocities '
print '# so that they can be found across multiple epochs. The Filt column '
print '# shows what filters this star should NOT be used in '
print '# (K=speckle, Kp,Lp,Ms = NIRC2). The PSF column specifies whether '
print '# this is a PSF star (1) or just a secondary source (0).'
print '#%-12s %-4s %-7s %-7s %-7s %-7s %-8s %-4s %-4s' % \
('Name', 'Mag', 'Xarc', 'Yarc', 'Vx', 'Vy', 't0', 'Filt', 'PSF?')
for star in stars:
print '%-13s %4.1f %7.3f %7.3f %7.3f %7.3f %8.3f %-4s %1d' % \
(star['name'], star['mag'], star['x'], star['y'],
0.0, 0.0, 2009.0, '-', 1)
def plotConfusion(confusion,
map,
grid_spacing,
title,
file_name,
inset=None,
fudge=16):
pylab.figure()
axmain = pylab.axes()
gs_x, gs_y = grid_spacing
confusion_cmap_dict = {
'red': (
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
'green': (
(0.0, 0.5, 0.5),
(0.25, 0.5, 0.0),
(0.5, 0.0, 1.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0),
),
'blue': (
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
}
confusion_cmap = LinearSegmentedColormap('confusion', confusion_cmap_dict,
256)
nx, ny = confusion.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=0, vmax=3)
tick_locs = [0.375 + 0.75 * l for l in range(4)]
tick_labels = ["Correct\nNegative", "False\nAlarm", "Miss", "Hit"]
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
drawPolitical(map, scale_len=25)
if inset:
lb_y, lb_x = [b.start for b in inset]
ub_y, ub_x = [b.stop + fudge for b in inset]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp],
ys[inset_exp],
confusion[inset_exp],
cmap=confusion_cmap,
vmin=0,
vmax=3)
drawPolitical(map)
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def Figure8(self):
self.fig = pylab.figure(facecolor='w',figsize=(14, 12),dpi=800)
gs = gridspec.GridSpec(57, 36)
gs.update(left=0.05, right=0.95, top=0.95, bottom=0.05,wspace=0.2,hspace=0.2)
picked_stcs = [(53,47),(53,49),(55,47),(51,63),(49,53),(47,63)]
ax = pylab.subplot(gs[1:19,9:27])
self.plot_average_oct(independent=False)
pylab.ylabel('Normalized response', fontsize=20)
ax = pylab.subplot(gs[22:32,1:11])
self.plot_orientation_contrast_tuning(picked_stcs[0][0], picked_stcs[0][1],'',independent=False)
remove_x_tick_labels()
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[22:32,13:23])
self.plot_orientation_contrast_tuning(picked_stcs[1][0], picked_stcs[1][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[22:32,25:35])
self.plot_orientation_contrast_tuning(picked_stcs[2][0], picked_stcs[2][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,1:11])
self.plot_orientation_contrast_tuning(picked_stcs[3][0], picked_stcs[3][1],'',independent=False)
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[34:44,13:23])
self.plot_orientation_contrast_tuning(picked_stcs[4][0], picked_stcs[4][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,25:35])
self.plot_orientation_contrast_tuning(picked_stcs[5][0], picked_stcs[5][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
# PLOT EXPERIMENTAL DATA
x,y = zip(*[(-88.694, 45.5367),(-81.8258, 45.0061),(-74.9654, 44.2076),(-68.0972, 43.677),(-61.2444, 42.6107),(-54.3686, 42.3479),(-47.5158, 41.2816),(-40.7245, 38.0724),(-34.0868, 29.5059),(-27.4415, 21.2073),(-20.7654, 13.9801),(-13.9357, 12.1102),(-7.006, 13.7225),(0.1156, 22.0313),(7.0299, 23.1078),(13.9364, 23.9165),(20.8891, 26.3325),(27.9186, 31.427),(34.9404, 36.2537),(41.9622, 41.0803),(48.9224, 43.7641),(55.7906, 43.2335),(62.6588, 42.7029),(69.5346, 42.4401),(76.4028, 41.9095), (83.2631, 41.1111),(90.1314, 40.5805)])
ox,oy = zip(*[(-89.7405, 9.108),(-82.8946, 9.3988),(-76.0487, 9.6896),(-69.1876, 10.5161),(-62.3417, 10.8069),(-55.4958, 11.0977),(-48.6423, 11.6562),(-41.7506, 13.5544),(-34.7597, 18.935),(-27.7841, 23.7797),(-20.8008, 28.8923),(-13.8557, 32.6657),(-6.8343, 39.1177),(0.1566, 44.4983),(6.8652, 39.9672),(13.505, 33.0251),(20.183, 27.4224),(26.8686, 22.0877),(33.539, 16.2172),(40.2475, 11.6861),(46.956, 7.155),(53.7942, 7.1779),(60.6172, 6.665),(67.4402, 6.1521),(74.2709, 5.9072),(81.1015, 5.6622),(87.2407, 5.147)])
x = numpy.array(x)
y = numpy.array(y)
oy = numpy.array(oy)
x = x/360.0*numpy.pi*2
ax = pylab.subplot(gs[47:57,5:15])
ax.plot(x,y,lw=3,color='r')
ax.plot(x,oy,lw=3,color='b')
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
ax.set_ylim(0,50)
ax.set_yticks([0,25,50])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels([r'-$\frac{\pi}{2}$',r'0',r'$\frac{\pi}{2}$'], fontProperties)
ax.set_yticklabels(ax.get_yticks(), fontProperties)
x,y = zip(*[(-90.5455, 0.6447),(-67.5208, 0.6732),(-45.9417, 0.572),(-22.6714, 0.4924),(-0.5531, 0.3234),(22.8077, 0.5215),(45.0244, 0.6539),(67.7793, 0.7153),(89.9339, 0.6573)])
x = numpy.array(x)
y = numpy.array(y)
x = x/360.0*numpy.pi*2
ax = pylab.subplot(gs[47:57,19:29])
ax.plot(x,y,lw=3,color='r')
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
ax.set_ylim(0,1.0)
ax.set_yticks([0,0.5,1.0])
ax.set_ylabel('Relative response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels([r'-$\frac{\pi}{2}$',r'0',r'$\frac{\pi}{2}$'], fontProperties)
ax.set_yticklabels(ax.get_yticks(), fontProperties)
release_fig("Figure8")
def plotMixtureStructure(mix,headers,transpose=1):
plot = numpy.zeros(( mix.G,mix.dist_nr ) )
for i in range(mix.dist_nr):
#print "-------------\n",headers[i]
#print mix.leaders[i]
#print mix.groups[i]
# check for noise variables
if len(mix.leaders[i]) == 1:
l = mix.leaders[i][0]
for g in range(mix.G):
plot[g,i] = 1
else:
for l in mix.leaders[i]:
#if len(mix.groups[i][l]) == 0:
# plot[l,i] = 2
# else:
plot[l,i] = l+3
for g in mix.groups[i][l]:
plot[g,i] = l+3
for j in range(mix.dist_nr):
t = {}
t[2] = 2
t[1] = 1
index = 3
v_list = []
for v in plot[:,j]:
if v == 2:
continue
elif v == 1:
break
else:
if v not in v_list:
v_list.append(v)
v_list.sort()
#print plot[:,j]
#print v_list
for v in v_list:
t[v] = index
index +=1
for i in range(mix.G):
plot[i,j] = t[plot[i,j]]
#for p in plot:
# print p.tolist()
#print plot
#pylab.subplot(1,2,1)
#x = numpy.array([0.0,5.0],dtype='Float64')
#y = numpy.array([0.0,5.0],dtype='Float64')
#pylab.plot(x,y,'o')
#pylab.subplot(1,2,2)
z = numpy.array(plot)
if transpose:
#print z.shape
z = z.transpose()
#print z.shape
mat = pylab.matshow(z)
# pylab.setp(mat)
# print mat.axes
# print mat.figure
pylab.grid(True,linestyle='-',linewidth=0.5)
# set xticks
#pylab.xticks( numpy.arange(len(headers))+0.5,['']*len(headers), size=12)
xtickpos = numpy.arange(0.0, mix.G+1,0.5)
temp = zip(range(mix.G), ['']*mix.G)
xticklabels = []
for tt in temp:
xticklabels += list(tt)
pylab.xticks( xtickpos,xticklabels) # rotation='vertical',size=12
#print xtickpos
#print xticklabels
# remove grid lines for tick lines at label positions
xgridlines = pylab.getp(pylab.gca(), 'xgridlines')
#print xgridlines
for j in range(len(xticklabels)):
if xticklabels[j] != '':
xgridlines[j].set_linestyle('None')
# set yticks
ytickpos = numpy.arange(0.0, len(headers),0.5)
temp = zip(headers, ['']*len(headers))
yticklabels = []
for tt in temp:
yticklabels += list(tt)
pylab.yticks( ytickpos, yticklabels, size=8)
# remove grid lines for tick lines at label positions
ygridlines = pylab.getp(pylab.gca(), 'ygridlines')
#print len(ygridlines), len(ytickpos), len(yticklabels)
for j,yl in enumerate(ygridlines):
if yticklabels[j] != '':
yl.set_linestyle('None')
#pylab.setp(ygridlines, 'linestyle', 'None')
#pylab.yticks( numpy.arange(len(headers))+0.5)
# loc, ll = pylab.yticks()
# for lll in ll:
# print lll,lll.get_dashlength()
# #lll.set_dashlength(-1)
#plotMixture.pylab.figtext(0.15,0.5,'T1',size=20, weight='bold')
#plotNormalMixtureDensitture.pylab.figtext(0.925,0.5,'T1',size=20, weight='bold')
#start = 0.15
# start = 0.18
# end = 0.925
#
# space = end - start
# step = space / mix.dist_nr
#for i in range(0,mix.dist_nr):
# XXX HACK for two components
#pylab.figtext(0.12,0.63,'$C_1$',size=20, weight='bold')
#pylab.figtext(0.12,0.26,'$C_2$',size=20, weight='bold')
else:
fig = pylab.matshow(z)
pylab.grid(True,linestyle='-',linewidth=0.5)
#pylab.yticks(numpy.arange(mix.G+1) )
#pylab.xticks( numpy.arange(len(plot[0])+1)+0.5,headers,rotation=90,size=12)
#fig.set_size_inches(20,20)
# set xticks
#pylab.xticks( numpy.arange(len(headers))+0.5,['']*len(headers), size=12)
xtickpos = numpy.arange(0.0, len(headers),0.5)
temp = zip(headers, ['']*len(headers))
xticklabels = []
for tt in temp:
xticklabels += list(tt)
pylab.xticks( xtickpos,xticklabels) # rotation='vertical',size=12
print len(xtickpos), xtickpos.tolist()
print len(xticklabels), xticklabels
# remove grid lines for tick lines at label positions
xgridlines = pylab.getp(pylab.gca(), 'xgridlines')
print xgridlines
for j in range(len(xticklabels)):
if xticklabels[j] != '':
xgridlines[j].set_linestyle('None')
# set yticks
ytickpos = numpy.arange(0.0, mix.G,0.5)
temp = zip(range(mix.G), ['']*mix.G)
yticklabels = []
for tt in temp:
yticklabels += list(tt)
pylab.yticks( ytickpos, yticklabels, size=12)
print len(ytickpos), ytickpos.tolist()
print len(yticklabels), yticklabels
# remove grid lines for tick lines at label positions
ygridlines = pylab.getp(pylab.gca(), 'ygridlines')
#print len(ygridlines), len(ytickpos), len(yticklabels)
for j,yl in enumerate(ygridlines):
if yticklabels[j] != '':
yl.set_linestyle('None')
def plot_stc_time_point(stc,
subject,
limits=[5, 10, 15],
time_index=0,
surf='inflated',
measure='dSPM',
subjects_dir=None):
"""Plot a time instant from a SourceEstimate using matplotlib
The same could be done with mayavi using proper 3D.
Parameters
----------
stc : instance of SourceEstimate
The SourceEstimate to plot.
subject : string
The subject name (only needed if surf is a string).
time_index : int
Time index to plot.
surf : str, or instance of surfaces
Surface to use (e.g., 'inflated' or 'white'), or pre-loaded surfaces.
measure : str
The label for the colorbar. None turns the colorbar off.
subjects_dir : str, or None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
"""
subjects_dir = get_subjects_dir(subjects_dir)
pl.figure(facecolor='k', figsize=(8, 5))
hemis = ['lh', 'rh']
if isinstance(surf, str):
surf = [
read_surface(
op.join(subjects_dir, subject, 'surf', '%s.%s' % (h, surf)))
for h in hemis
]
my_cmap = mne_analyze_colormap(limits)
for hi, h in enumerate(hemis):
coords = surf[hi][0][stc.vertno[hi]]
if hi == 0:
vals = stc_all_cluster_vis.lh_data[:, time_index]
else:
vals = stc_all_cluster_vis.rh_data[:, time_index]
ax = pl.subplot(1, 2, 1 - hi, axis_bgcolor='none')
pl.tick_params(labelbottom='off', labelleft='off')
flipper = -1 if hi == 1 else 1
sc = ax.scatter(flipper * coords[:, 1],
coords[:, 2],
c=vals,
vmin=-limits[2],
vmax=limits[2],
cmap=my_cmap,
edgecolors='none',
s=5)
ax.set_aspect('equal')
pl.axis('off')
try:
pl.tight_layout(0)
except:
pass
if measure is not None:
cax = pl.axes([0.85, 0.15, 0.025, 0.15], axisbg='k')
cb = pl.colorbar(sc, cax, ticks=[-limits[2], 0, limits[2]])
cb.set_label(measure, color='w')
pl.setp(pl.getp(cb.ax, 'yticklabels'), color='w')
pl.draw()
pl.show()
pl.gca().set_xticks(inh_range[1::2])
pl.gca().set_xticklabels(inh_range[1::2], size=labelsize)
if ext_count == 1:
pl.gca().set_xlabel('Fraction of BS neurons in STN', size=labelsize)
if ext_count == 2:
cax = fig.add_axes([0.895, 0.15, 0.025, 0.25])
cbar = pl.colorbar(orientation='vertical', cax=cax, cmap=cm.coolwarm)
cbar.set_label('Spectral entropy of STN',
position=(1.3, 0.5),
fontsize=labelsize,
rotation='vertical')
cbar.set_ticks(cr)
pl.gca().set_yticks([])
cticks = pl.getp(cbar.ax.axes, 'yticklabels')
pl.setp(cticks, fontsize=labelsize)
if ext_count == 0:
pl.gca().set_yticks([0., 0.5, 1.0])
pl.gca().set_yticklabels([0., 0.5, 1.0], size=labelsize)
pl.gca().set_ylabel('Fraction of BS neurons in GPe', size=labelsize)
else:
ax1.set_yticks([])
ax1.text(-0.1, 1.2, tex_lis[ext_count], size=labelsize + 5)
#ax1 = fig.add_axes([0.7,0.1,0.225,0.35])
#for inh_count, inh_val in enumerate(inh_range):
#spec_lis = []
#for exc_count, exc_val in enumerate(exc_range):
#ad1 = get_sim_3d({'pg_rate_exc':4900., 'pg_rate_inh':1800.,'exc2_ratio':exc_val, 'inh2_ratio':inh_val})
#xx,xx,xx,peak_freq_gpe, peak_pow_gpe = ad1.psd(time_range = [ad1.pars['T_wup'],ad1.pars['T_wup']+ ad1.pars['T_sim']],pop_id = popul)
def __execute_baseline(self):
#print "calibration begins..."
#select the totalpower data for antenna1,2=1 (DV01, vertex antenna)
# use generic pylab
if type(self.antid) == list and len(self.antid) > 2:
#print "Assume the antenna selection is all..."
casalog.post( "Assume the antenna selection is all..." )
self.open_table(self.infile)
ndat = 0
pl.ioff()
# determine the size of processing data
subtb = self.table.query('any(ANTENNA1==%s && ANTENNA2==%s)' % (self.antid,self.antid))
nr = subtb.nrows()
#nr = len(data)*len(data[0])
#ndatcol = numpy.zeros((npol,nr),dtype=numpy.float)
ndatcol = subtb.getcol(self.datacol)
(l,m,n) = ndatcol.shape
# m (= nchan) should be 1 since the task is specifically designed for total power data
ndatcol.reshape(l,n)
for np in range(len(self.selcorrtypeind)):
pl.figure(np+1)
pl.clf()
pl.subplot(311)
#pl.title(infile+' Ant:'+str(antlist.values()))
pl.ylabel(self.datacol,fontsize='smaller')
symbols=['b.','c.']
#print "Arranging data by scans..."
casalog.post( "Arranging data by scans..." )
data = []
flag = []
ndat0 = 0
ndat = 0
if self.selnpol == 1: np = self.selpol
pl.title(self.infile.rstrip('/')+' Ant:'+self.antnameused+' '+self.corrtypestr[np])
casalog.post( "select %s data..." % self.corrtypestr[np] )
for i in xrange(self.nscan):
idata = []
iflag = []
#for j in xrange(nsubscan):
for j in xrange(self.nsubscan[i]):
# may be need to iterate on each antenna
# identify 'scan' by STATE ID
#selsubtb=tb.query('any(ANTENNA1==%s && ANTENNA2==%s) && SCAN_NUMBER==%s && STATE_ID==%s' % (antid,antid,scans[i],subscans[j]))
selsubtb = self.table.query('any(ANTENNA1==%s && ANTENNA2==%s) && SCAN_NUMBER==%s && STATE_ID==%s' % (self.antid,self.antid,self.scans[i],self.subscans[i][j]))
datcol = selsubtb.getcol(self.datacol)
flgcol = selsubtb.getcol("FLAG") # only channel flag is needed to be hanled in MS
selsubtb.close()
if self.npol > 1 and self.selnpol == 1:
#casalog.post( "select %s data..." % corrtypestr[selpol] )
rdatcol = datcol[self.selpol].real
flagcell = flgcol[self.selpol]
else:
rdatcol = datcol[self.selcorrtypeind[np]].real
flagcell = flgcol[self.selpol]
(m,n) = rdatcol.shape
if flagcell.shape != (m,n):
raise Exception, "Data conformation error. Shape of FLAG and %s differs." % self.datacol
rdatcol = rdatcol.reshape(n)
flagcell = flagcell.reshape(n)
#data.append(rdatcol)
idata.append(rdatcol)
iflag.append(flagcell)
ndat0 = ndat
ndat += len(rdatcol)
data.append(idata)
flag.append(iflag)
if abs(self.plotlevel) > 0:
pl.xlim(0,ndat)
t1 = pl.getp(pl.gca(), 'xticklabels')
t2 = pl.getp(pl.gca(), 'yticklabels')
pl.setp((t1,t2), fontsize='smaller')
mdat = 0
for i in xrange(self.nscan):
#for j in xrange(nsubscan):
for j in xrange(self.nsubscan[i]):
mdat0 = mdat
mdat += len(data[i][j])
masked_data = numpy.ma.masked_array(data[i][j], flag[i][j])
#pl.plot(xrange(mdat0,mdat),data[i][j],symbols[subscans[j]%2])
#pl.plot(xrange(mdat0,mdat), data[i][j], symbols[self.subscans[i][j]%2])
pl.plot(xrange(mdat0,mdat), masked_data, symbols[self.subscans[i][j]%2])
ax = pl.gca()
if i == 1:
leg = ax.legend(('even subscan no.', 'odd subscan no.'), numpoints=1, handletextsep=0.01)
for t in leg.get_texts():
t.set_fontsize('small')
pl.ion()
pl.plot()
#pl.draw()
### Calibration ############################################
# Do a zero-th order calibration by subtracting a baseline #
# from each scan to take out atmospheric effect. #
# The baseline fitting range specified by masklist must be #
# given and is the same for all the scans. #
############################################################
f = sd.fitter()
f.set_function(lpoly = self.blpoly)
#print "Processing %s %s scans" % (corrtypestr[np], nscan)
casalog.post( "Processing %s %s scans" % (self.corrtypestr[np], self.nscan) )
cdat = numpy.array([])
pl.subplot(312)
pl.ioff()
for i in xrange(self.nscan):
casalog.post( "Processing Scan#=%s" % i )
#for j in xrange(nsubscan):
for j in xrange(self.nsubscan[i]):
masks = numpy.zeros(len(data[i][j]), dtype=numpy.int)
if self.left_mask >= len(data[i][j]) \
or self.right_mask >= len(data[i][j]):
msg = "Too large mask. All data will be used for baseline subtraction.\n The fitting may not be correct since it might confuse signal component as a background..."
casalog.post(msg, "WARN")
else:
msg = "Subtracting baselines, set masks for fitting at row ranges: [0,%s] and [%s,%s] " % (self.left_mask-1, len(masks)-self.right_mask, len(masks)-1)
casalog.post(msg, "INFO")
#Use edge channels for fitting excluding flagged integrations.
#masks[:self.left_mask] = True
#masks[-self.right_mask:] = True
masks[:self.left_mask] = (flag[i][j][:self.left_mask]==False)
masks[-self.right_mask:] = (flag[i][j][-self.right_mask:]==False)
x = xrange(len(data[i][j]))
if self.plotlevel > 1:
pl.cla()
pl.ioff()
pl.title('scan %s subscan %s'%(self.scans[i],self.subscans[i][j]))
#pl.plot(x, data[i][j], 'b')
pl.plot(x, numpy.ma.masked_array(data[i][j], flag[i][j]), 'b')
f.set_data(x, data[i][j], mask=masks)
f.fit()
#f.plot(residual=True, components=[1], plotparms=True)
fitd=f.get_fit()
data[i][j] = data[i][j]-fitd
if self.plotlevel > 1:
pl.ion()
pl.plot(xrange(len(fitd)), fitd, 'r-')
pl.draw()
pl.cla()
pl.ioff()
pl.title('scan %s subscan %s'%(self.scans[i], self.subscans[i][j]))
pl.ion()
#pl.plot(x, data[i][j], 'g')
pl.plot(x, numpy.ma.masked_array(data[i][j], flag[i][j]), 'g')
pl.draw()
if interactive: check=raw_input('Hit return for Next\n')
cdat = numpy.concatenate([cdat,data[i][j]])
ndatcol[np] = cdat
del data
subtb.close()
self.close_table()
self.open_table(self.infile, nomodify=False)
# put the corrected data to CORRECTED_DATA column in MS
# assuming the data for the vertex are stored in every other
# row starting from 2nd row for the vertex antenna....
# For the alcatel startrow=0, for len(antid) >1,
# it assumes all the data to be corrected.
if type(self.antid) == int:
startrow = self.antid
rowincr = self.nant
else:
startrow = 0
rowincr = 1
casalog.post( "Storing the corrected data to %s column in the MS..."%self.datacol )
casalog.post( "Note that %s will be overwritten"%self.datacol )
cdatm = ndatcol.reshape(self.npol,1,len(cdat))
self.table.putcol(self.datacol, cdatm, startrow=startrow, rowincr=rowincr)
self.close_table()
# calibration end
#
# plot corrected data
if abs(self.plotlevel) > 0:
casalog.post( "plotting corrected data..." )
self.__plot(subplot=313, title=False, ylabel='CORRECTED_DATA')
ax.set_ylim((0.0, MaxVal))
mindate = min(pylab.date2num(datetimes[-1]), (pylab.date2num(datetimes[0])) )
maxdate = max(pylab.date2num(datetimes[-1]), (pylab.date2num(datetimes[0])) )
ax.set_xlim( (mindate, maxdate) )
#noLabels = NullLocator()
#Labels = NullFormatter()
ax.xaxis.set_major_locator(days)
ax.xaxis.set_major_formatter(daysFmt)
labels = ax.xaxis.get_ticklabels()
print labels
#print pylab.setp(labels)
print pylab.getp(labels[0], "fontsize")
##pylab.setp(labels,
## rotation=45,
## horizontalalignment='right',
## dashlength=3,
## dashpad=0,
## fontsize=10)
for l in labels:
print "setting properties"
l.set_rotation(45)
l.set_horizontalalignment('right')
l.set_dashlength(3)
l.set_dashpad(0)
l.set_fontsize(16)
def plotMultiplePMFs(*InferenceObjects, **kwargs):
"""
Plot multiple psychometric functions
:Parameters:
*InferenceObjects* :
The Inference Objects that should be plotted. If the inference objects contain
information about themselves, this information is used.
*ax* :
the axis object where the plot should go
*xlabel* :
text to be written on the y axis
*ylabel* :
text to be written on the x axis
*ci* :
boolean indicating whether credibility intervals should be drawn
by default, this is False
:Example:
This example shows how to plot multiple psychometric functions
>>> d0 = [[0, 28, 50], [2, 33, 50], [4, 38, 50], [6, 45, 50], [8, 45, 50], [10, 49, 50]]
>>> d1 = [[0, 22, 50], [2, 34, 50], [4, 31, 50], [6, 42, 50], [8, 42, 50], [10, 46, 50]]
>>> d2 = [[0, 26, 50], [2, 31, 50], [4, 38, 50], [6, 47, 50], [8, 49, 50], [10, 49, 50]]
>>> constraints = ("","","Uniform(0,.1)")
>>> B0 = BootstrapInference ( d0, priors=constraints,plotprm={"color": "r", "label": "Condition 0"} )
>>> B1 = BootstrapInference ( d1, priors=constraints, plotprm={"color": "b","label": "Condition 1"} )
>>> B2 = BootstrapInference ( d2, priors=constraints, plotprm={"color": "b","label": "Condition 2"} )
>>> plotMultiplePMFs ( B0, B1, B2 )
"""
ax = kwargs.setdefault("ax", None)
if ax is None:
ax = p.axes()
pmflines = []
pmflabels = []
pmfdata = []
for pmf in InferenceObjects:
l, d = plotPMF(pmf, showaxes=False, showdesc=False, ax=ax)[:2]
pmflines.append(l)
pmfdata.append(d)
pmflabels.append(pmf.label)
if kwargs.setdefault("ci", False):
plotThres(pmf, ax, color=pmf.color)
ylabel_text = kwargs.setdefault("ylabel", None)
if ylabel_text is None:
if pmf.model["nafc"] < 2:
ylabel_text = "P(Yes)"
else:
ylabel_text = "P(correct)"
# Determine tics
# p.setp(ax,frame_on=False,ylim=(-.05,1.05))
xtics = p.getp(ax, 'xticks')
ytics = list(p.getp(ax, 'yticks'))
# Clean up ytics
for k, yt in enumerate(ytics):
if yt < 0 or yt > 1:
ytics.pop(k)
ytics = N.array(ytics)
drawaxes(ax, xtics, "%g", ytics, "%g",
kwargs.setdefault("xlabel", "stimulus intensity"), ylabel_text)
# Draw legend
ax.legend(pmflines, pmflabels, 'lower right')
return pmflines, pmfdata
def animate_interactive(data,
t=None,
dim_order=(0, 1, 2),
fps=10.0,
title=None,
xlabel='x',
ylabel='y',
font_size=24,
color_bar=0,
colorbar_label=None,
sloppy=True,
fancy=False,
range_min=None,
range_max=None,
extent=[-1, 1, -1, 1],
shade=False,
azdeg=0,
altdeg=65,
arrowsX=None,
arrowsY=None,
arrows_resX=10,
arrows_resY=10,
arrows_pivot='mid',
arrows_width=0.002,
arrows_scale=5,
arrows_color='black',
plot_arrows_grid=False,
movie_file=None,
bitrate=1800,
keep_images=False,
figsize=(8, 7),
dpi=300,
**kwimshow):
"""
Assemble a 2D animation from a 3D array.
call signature::
animate_interactive(data, t=None, dim_order=(0, 1, 2),
fps=10.0, title=None, xlabel='x', ylabel='y',
font_size=24, color_bar=0, colorbar_label=None,
sloppy=True, fancy=False,
range_min=None, range_max=None, extent=[-1, 1, -1, 1],
shade=False, azdeg=0, altdeg=65,
arrowsX=None, arrowsY=None, arrows_resX=10, arrows_resY=10,
arrows_pivot='mid', arrows_width=0.002, arrows_scale=5,
arrows_color='black', plot_arrows_grid=False,
movie_file=None, bitrate=1800, keep_images=False,
figsize=(8, 7), dpi=300,
**kwimshow)
Assemble a 2D animation from a 3D array. *data* has to be a 3D array of
shape [nt, nx, ny] and who's time index has the same dimension as *t*.
The time index of *data* as well as its x and y indices can be changed
via *dim_order*.
Keyword arguments:
*dim_order*:
Ordering of the dimensions in the data array (t, x, y).
*fps*:
Frames per second of the animation.
*title*:
Title of the plot.
*xlabel*:
Label of the x-axis.
*ylabel*:
Label of the y-axis.
*font_size*:
Font size of the title, x and y label.
The size of the x- and y-ticks is 0.5*font_size and the colorbar ticks'
font size is 0.5*font_size.
*color_bar*: [ 0 | 1 ]
Determines how the colorbar changes:
(0 - no cahnge; 1 - adapt extreme values).
*colorbar_label*:
Label of the color bar.
*sloppy*: [ True | False ]
If True the update of the plot lags one frame behind. This speeds up the
plotting.
*fancy*: [ True | False ]
Use fancy font style.
*range_min*, *range_max*:
Range of the colortable.
*extent*: [ None | (left, right, bottom, top) ]
Limits for the axes (domain).
*shade*: [ False | True ]
If True plot a shaded relief instead of the usual colormap.
Note that with this option cmap has to be specified like
cmap = plt.cm.hot instead of cmap = 'hot'. Shading cannot
be used with the color_bar = 0 option.
*azdeg*, *altdeg*:
Azimuth and altitude of the light source for the shading.
*arrowsX*:
Data containing the x-component of the arrows.
*arrowsY*:
Data containing the y-component of the arrows.
*arrows_resXY*:
Plot every arrows_resXY arrow in x and y.
*arrows_pivot*: [ 'tail' | 'middle' | 'tip' ]
The part of the arrow that is used as pivot point.
*arrows_width*:
Width of the arrows.
*arrows_scale*:
Scaling of the arrows.
*arrows_color*:
Color of the arrows.
*plot_arrows_grid*: [ False | True ]
If 'True' the grid where the arrows are aligned to is shown.
*movie_file*: [ None | string ]
The movie file where the animation should be saved to.
If 'None' no movie file is written. Requires 'ffmpeg' to be installed.
*bitrate*:
Bitrate of the movie file. Set to higher value for higher quality.
*keep_images*: [ False | True ]
If 'True' the images for the movie creation are not deleted.
*figsize*:
Size of the figure in inches.
*dpi*:
Dots per inch of the frame.
**kwimshow:
Remaining arguments are identical to those of pylab.imshow. Refer to that help.
"""
try:
import thread
except:
import _thread as thread
# We need to define these variables as globals, as they are being used
# by various functions.
global time_step, time_slider, pause
global fig, axes, image, colorbar, arrows, manager, n_times, movie_files
global rgb, plot_arrows
if title is None:
title = ''
def plot_frame():
"""
Plot the current frame.
"""
global time_step, axes, colorbar, arrows, manager, rgb
# Define the plot title.
if not movie_file is None:
axes.set_title(title + r'$\quad$' +
r'$t={0:.4e}$'.format(t[time_step]),
fontsize=font_size)
# Update the image data.
if not shade:
image.set_data(data[time_step, :, :])
else:
image.set_data(rgb[time_step, :, :, :])
# Update the colorbar.
if color_bar == 0:
pass
if color_bar == 1:
colorbar.set_clim(vmin=data[time_step, :, :].min(),
vmax=data[time_step, :, :].max())
colorbar.draw_all()
# Update the arrows data.
if plot_arrows:
arrows.set_UVC(U=arrowsX[time_step, ::arrows_resX, ::arrows_resY],
V=arrowsY[time_step, ::arrows_resX, ::arrows_resY])
if not sloppy or (not movie_file is None):
manager.canvas.draw()
def play(thread_name):
"""
Play the movie.
"""
import time
global time_step, time_slider, pause, fig, axes, n_times, movie_files
pause = False
while (time_step < n_times) and (not pause):
# Write the image files for the movie.
if not movie_file is None:
plot_frame()
frame_name = '{0}{1:06}.png'.format(movie_file, time_step)
fig.savefig(frame_name, dpi=dpi)
movie_files.append(frame_name)
else:
time_start = time.clock()
time_slider.set_val(t[time_step])
# Wait for the next frame (fps).
while (time.clock() - time_start < 1.0 / fps):
pass
time_step += 1
time_step -= 1
def play_thread(event):
"""
Call the play function as a separate thread (for GUI).
"""
global pause
if pause:
try:
thread.start_new_thread(play, ("play_thread", ))
except:
print("Error: unable to start play thread.")
def pausing(event):
global pause
pause = True
def reverse(event):
global time_step, time_slider
time_step -= 1
if time_step < 0:
time_step = 0
# Plot the frame and update the time slider.
time_slider.set_val(t[time_step])
def forward(event):
global time_step, time_slider
time_step += 1
if time_step > len(t) - 1:
time_step = len(t) - 1
# Plot the frame and update the time slider.
time_slider.set_val(t[time_step])
import numpy as np
import pylab as plt
pause = True
plot_arrows = False
# Check if the data has the right dimensions.
if (data.ndim != 3 and data.ndim != 4):
print("Error: data dimensions are invalid: {0} instead of 3.".format(
data.ndim))
return -1
# Transpose the data according to dim_order.
unordered_data = data
data = np.transpose(unordered_data, dim_order)
del (unordered_data)
# Check if arrows should be plotted.
if not (arrowsX is None) and not (arrowsY is None):
if (isinstance(arrowsX, np.ndarray)
and isinstance(arrowsY, np.ndarray)):
if arrowsX.ndim == 3:
# Transpose the data according to dim_order.
unordered_data = arrowsX
arrowsX = np.transpose(unordered_data, dim_order)
del (unordered_data)
if arrowsY.ndim == 3:
# Transpose the data according to dim_order.
unordered_data = arrowsY
arrowsY = np.transpose(unordered_data, dim_order)
unordered_data = []
# Check if the dimensions of the arrow arrays match each other.
if arrowsX.shape != arrowsY.shape:
print(
"Error: dimensions of arrowX do not match with dimensions of arrowY."
)
return -1
else:
plot_arrows = True
else:
print("Warning: arrowsX and/or arrowsY are of invalid type.")
# Check if time array has the right length.
n_times = len(t)
if n_times != data.shape[0]:
print(
"Error: length of time array does not match length of data array.")
return -1
if plot_arrows:
if (n_times != arrowsX.shape[0]) or (n_times != arrowsY.shape[0]):
print(
"error: length of time array does not match length of arrows array."
)
return -1
# Check if fps is positive.
if fps < 0:
print("Error: fps is not positive, fps = {0}.".format(fps))
return -1
# Determine the size of the data array.
nX = data.shape[1]
nY = data.shape[2]
# Determine the minimum and maximum values of the data set.
if not range_min:
range_min = np.min(data)
if not range_max:
range_max = np.max(data)
# Setup the plot.
if fancy:
plt.rc('text', usetex=True)
plt.rc('font', family='arial')
else:
plt.rc('text', usetex=False)
plt.rc('font', family='sans')
if not movie_file is None:
fig = plt.figure(figsize=figsize)
axes = plt.axes([0.15, 0.1, .70, .85])
else:
fig = plt.figure(figsize=figsize)
axes = plt.axes([0.1, 0.3, .80, .65])
# Set up canvas of the plot.
axes.set_title(title, fontsize=font_size)
axes.set_xlabel(xlabel, fontsize=font_size)
axes.set_ylabel(ylabel, fontsize=font_size)
plt.xticks(fontsize=0.5 * font_size)
plt.yticks(fontsize=0.5 * font_size)
if shade:
plane = np.zeros([nX, nY, 3])
else:
plane = np.zeros([nX, nY])
# Apply shading.
if shade:
from matplotlib.colors import LightSource
ls = LightSource(azdeg=azdeg, altdeg=altdeg)
rgb = []
# Shading can be only used with color_bar=1 or color_bar=2 at the moment.
if color_bar == 0:
color_bar = 1
# Check if colormap is set, if not set it to 'copper'.
if 'cmap' not in kwimshow.keys():
kwimshow['cmap'] = plt.cm.copper
for i in range(data.shape[0]):
tmp = ls.shade(data[i, :, :], kwimshow['cmap'])
rgb.append(tmp.tolist())
rgb = np.array(rgb)
del (tmp)
# Calibrate the displayed colors for the data range.
image = axes.imshow(plane,
vmin=range_min,
vmax=range_max,
origin='lower',
extent=extent,
**kwimshow)
colorbar = fig.colorbar(image)
colorbar.set_label(colorbar_label, fontsize=font_size, labelpad=10)
# Change the font size of the colorbar's ytickslabels.
cbytick_obj = plt.getp(colorbar.ax.axes, 'yticklabels')
plt.setp(cbytick_obj, fontsize=0.5 * font_size)
# Plot the arrows.
if plot_arrows:
# Prepare the mesh grid where the arrows will be drawn.
arrow_grid = np.meshgrid(
np.arange(
extent[0], extent[1],
float(extent[1] - extent[0]) * arrows_resX /
(data.shape[2] - 1)),
np.arange(
extent[2], extent[3],
float(extent[3] - extent[2]) * arrows_resY /
(data.shape[1] - 1)))
arrows = axes.quiver(arrow_grid[0],
arrow_grid[1],
arrowsX[0, ::arrows_resX, ::arrows_resY],
arrowsY[0, ::arrows_resX, ::arrows_resY],
units='width',
pivot=arrows_pivot,
width=arrows_width,
scale=arrows_scale,
color=arrows_color)
# Plot the grid for the arrows.
if plot_arrows_grid:
axes.plot(arrow_grid[0], arrow_grid[1], 'k.')
# For real-time image display.
if (not sloppy) or (not movie_file is None):
manager = plt.get_current_fig_manager()
manager.show()
time_step = 0
if not movie_file is None:
import os
movie_files = []
# Start the animation.
play('no_thread')
# Write the movie file.
ffmpeg_command = "ffmpeg -r {0} -i {1}%6d.png -vcodec mpeg4 -b:v {2} -q:v 0 {3}.avi".format(
fps, movie_file, bitrate, movie_file)
os.system(ffmpeg_command)
# Clean up the image files.
if not keep_images:
print("Cleaning up files.")
for fname in movie_files:
os.remove(fname)
else:
# Set up the gui.
plt.ion()
plt.subplots_adjust(bottom=0.2)
# axes_play = plt.axes([0.1, 0.05, 0.15, 0.05])
# button_play = plt.Button(axes_play, 'play', color='lightgoldenrodyellow',
# hovercolor='0.975')
# button_play.on_clicked(play_thread)
# axes_pause = plt.axes([0.3, 0.05, 0.15, 0.05])
# button_pause = plt.Button(axes_pause, 'pause', color='lightgoldenrodyellow',
# hovercolor='0.975')
# button_pause.on_clicked(pausing)
# axes_reverse = plt.axes([0.5, 0.05, 0.15, 0.05])
axes_reverse = plt.axes([0.1, 0.05, 0.3, 0.05])
button_reverse = plt.Button(axes_reverse,
'reverse',
color='lightgoldenrodyellow',
hovercolor='0.975')
button_reverse.on_clicked(reverse)
# axes_forward = plt.axes([0.7, 0.05, 0.15, 0.05])
axes_forward = plt.axes([0.5, 0.05, 0.3, 0.05])
button_forward = plt.Button(axes_forward,
'forward',
color='lightgoldenrodyellow',
hovercolor='0.975')
button_forward.on_clicked(forward)
# Create the time slider.
time_slider_axes = plt.axes([0.2, 0.12, 0.6, 0.03],
facecolor='lightgoldenrodyellow')
time_slider = plt.Slider(time_slider_axes,
'time',
t[0],
t[-1],
valinit=t[0])
def update(val):
global time_step
# Find the closest time step to the slider time value.
for i in range(len(t)):
if t[i] < time_slider.val:
time_step = i
if (time_step != len(t) - 1):
if (t[time_step + 1] - time_slider.val) < (time_slider.val -
t[time_step]):
time_step += 1
plot_frame()
time_slider.on_changed(update)
plt.show()
print("done")
# return button_play, button_pause, button_reverse, button_forward
return button_reverse, button_forward
def animate_interactive(data, t = [], dimOrder = (0,1,2),
fps = 10.0, title = '', xlabel = 'x', ylabel = 'y',
fontsize = 24, cBar = 0, sloppy = True,
rangeMin = [], rangeMax = [], extent = [-1,1,-1,1],
shade = False, azdeg = 0, altdeg = 65,
arrowsX = np.array(0), arrowsY = np.array(0), arrowsRes = 10,
arrowsPivot = 'mid', arrowsWidth = 0.002, arrowsScale = 5,
arrowsColor = 'black', plotArrowsGrid = False,
movieFile = '', bitrate = 1800, keepImages = False,
figsize = (8, 7), dpi = None,
**kwimshow):
"""
Assemble a 2D animation from a 3D array.
call signature::
animate_interactive(data, t = [], dimOrder = (0,1,2),
fps = 10.0, title = '', xlabel = 'x', ylabel = 'y',
fontsize = 24, cBar = 0, sloppy = True,
rangeMin = [], rangeMax = [], extent = [-1,1,-1,1],
shade = False, azdeg = 0, altdeg = 65,
arrowsX = np.array(0), arrowsY = np.array(0), arrowsRes = 10,
arrowsPivot = 'mid', arrowsWidth = 0.002, arrowsScale = 5,
arrowsColor = 'black', plotArrowsGrid = False,
movieFile = '', bitrate = 1800, keepImages = False,
figsize = (8, 7), dpi = None,
**kwimshow)
Assemble a 2D animation from a 3D array. *data* has to be a 3D array who's
time index has the same dimension as *t*. The time index of *data* as well
as its x and y indices can be changed via *dimOrder*.
Keyword arguments:
*dimOrder*: [ (i,j,k) ]
Ordering of the dimensions in the data array (t,x,y).
*fps*:
Frames per second of the animation.
*title*:
Title of the plot.
*xlabel*:
Label of the x-axis.
*ylabel*:
Label of the y-axis.
*fontsize*:
Font size of the title, x and y label.
The size of the x- and y-ticks is 0.7*fontsize and the colorbar ticks's
font size is 0.5*fontsize.
*cBar*: [ 0 | 1 | 2 ]
Determines how the colorbar changes:
(0 - no cahnge; 1 - keep extreme values constant; 2 - change extreme values).
*sloppy*: [ True | False ]
If True the update of the plot lags one frame behind. This speeds up the
plotting.
*rangeMin*, *rangeMax*:
Range of the colortable.
*extent*: [ None | scalars (left, right, bottom, top) ]
Data limits for the axes. The default assigns zero-based row,
column indices to the *x*, *y* centers of the pixels.
*shade*: [ False | True ]
If True plot a shaded relief plot instead of the usual colormap.
Note that with this option cmap has to be specified like
cmap = plt.cm.hot instead of cmap = 'hot'. Shading cannot
be used with the cBar = 0 option.
*azdeg*, *altdeg*:
Azimuth and altitude of the light source for the shading.
*arrowsX*:
Data containing the x-component of the arrows.
*arrowsy*:
Data containing the y-component of the arrows.
*arrowsRes*:
Plot every arrowRes arrow.
*arrowsPivot*: [ 'tail' | 'middle' | 'tip' ]
The part of the arrow that is at the grid point; the arrow rotates
about this point.
*arrowsWidth*:
Width of the arrows.
*arrowsScale*:
Scaling of the arrows.
*arrowsColor*:
Color of the arrows.
*plotArrowsGrid*: [ False | True ]
If 'True' the grid where the arrows are aligned to is shown.
*movieFile*: [ None | string ]
The movie file where the animation should be saved to.
If 'None' no movie file is written. Requires 'mencoder' to be installed.
*bitrate*:
Bitrate of the movie file. Set to higher value for higher quality.
*keepImages*: [ False | True ]
If 'True' the images for the movie creation are not deleted.
*figsize*:
Size of the figure in inches.
*dpi*:
Dots per inch of the frame.
**kwimshow:
Remaining arguments are identical to those of pylab.imshow. Refer to that help.
"""
global tStep, sliderTime, pause
# plot the current frame
def plotFrame():
global tStep, sliderTime
if movieFile:
ax.set_title(title+r'$\quad$'+r'$t={0}$'.format(t[tStep]), fontsize = fontsize)
if shade == False:
image.set_data(data[tStep,:,:])
else:
image.set_data(rgb[tStep,:,:,:])
if (cBar == 0):
pass
if (cBar == 1):
colorbar.set_clim(vmin=data[tStep,:,:].min(), vmax=data[tStep,:,:].max())
if (cBar == 2):
colorbar.set_clim(vmin=data[tStep,:,:].min(), vmax=data[tStep,:,:].max())
colorbar.update_bruteforce(data[tStep,:,:])
if plotArrows:
arrows.set_UVC(U = arrowsX[tStep,::arrowsRes,::arrowsRes], V = arrowsY[tStep,::arrowsRes,::arrowsRes])
if (sloppy == False) or (movieFile):
manager.canvas.draw()
# play the movie
def play(threadName):
global tStep, sliderTime, pause
pause = False
while (tStep < nT) & (pause == False):
# write the image files for the movie
if movieFile:
plotFrame()
frameName = movieFile + '%06d.png'%tStep
fig.savefig(frameName, dpi = dpi)
movieFiles.append(frameName)
else:
start = time.clock()
# time slider
sliderTime.set_val(t[tStep])
# wait for the next frame (fps)
while (time.clock() - start < 1.0/fps):
pass # do nothing
tStep += 1
tStep -= 1
# call the play function as a separate thread (for GUI)
def play_thread(event):
global pause
if pause == True:
try:
thread.start_new_thread(play, ("playThread", ))
except:
print "Error: unable to start play thread"
def pausing(event):
global pause
pause = True
def reverse(event):
global tStep, sliderTime
tStep -= 1
if tStep < 0:
tStep = 0
# plot the frame and update the time slider
sliderTime.set_val(t[tStep])
def forward(event):
global tStep, sliderTime
tStep += 1
if tStep > len(t)-1:
tStep = len(t)-1
# plot the frame and update the time slider
sliderTime.set_val(t[tStep])
pause = True
plotArrows = False
# check if the data has the right dimensions
if (len(data.shape) != 3 and len(data.shape) != 4):
print 'error: data dimensions are invalid: {0} instead of 3'.format(len(data.shape))
return -1
# transpose the data according to dimOrder
unOrdered = data
data = np.transpose(unOrdered, dimOrder)
unOrdered = []
# check if arrows should be plotted
if len(arrowsX.shape) == 3:
# transpose the data according to dimOrder
unOrdered = arrowsX
arrowsX = np.transpose(unOrdered, dimOrder)
unOrdered = []
if len(arrowsY.shape) == 3:
# transpose the data according to dimOrder
unOrdered = arrowsY
arrowsY = np.transpose(unOrdered, dimOrder)
unOrdered = []
# check if the dimensions of the arrow arrays match each other
if ((len(arrowsX[:,0,0]) != len(arrowsY[:,0,0])) or (len(arrowsX[0,:,0]) != len(arrowsY[0,:,0])) or (len(arrowsX[0,0,:]) != len(arrowsY[0,0,:]))):
print 'error: dimensions of arrowX do not match with dimensions of arrowY'
return -1
else:
plotArrows = True
# check if time array has the right length
nT = len(t)
if (nT != len(data[:,0,0])):
print 'error: length of time array doesn\'t match length of data array'
return -1
if plotArrows:
if (nT != len(arrowX[:,0,0]) or nT != len(arrowX[:,0,0])):
print 'error: length of time array doesn\'t match length of arrows array'
return -1
# check if fps is positive
if (fps < 0.0):
print 'error: fps is not positive, fps = {0}'.format(fps)
return -1
# determine the size of the array
nX = len(data[0,:,0])
nY = len(data[0,0,:])
# determine the minimum and maximum values of the data set
if not(rangeMin):
rangeMin = np.min(data)
if not(rangeMax):
rangeMax = np.max(data)
# setup the plot
if movieFile:
width = figsize[0]
height = figsize[1]
plt.rc("figure.subplot", bottom=0.15)
plt.rc("figure.subplot", top=0.95)
plt.rc("figure.subplot", right=0.95)
plt.rc("figure.subplot", left=0.15)
fig = plt.figure(figsize = figsize)
ax = plt.axes([0.1, 0.1, .90, .85])
else:
width = figsize[0]
height = figsize[1]
plt.rc("figure.subplot", bottom=0.05)
plt.rc("figure.subplot", top=0.95)
plt.rc("figure.subplot", right=0.95)
plt.rc("figure.subplot", left=0.15)
fig = plt.figure(figsize = figsize)
ax = plt.axes([0.1, 0.25, .85, .70])
ax.set_title(title, fontsize = fontsize)
ax.set_xlabel(xlabel, fontsize = fontsize)
ax.set_ylabel(ylabel, fontsize = fontsize)
plt.xticks(fontsize = 0.7*fontsize)
plt.yticks(fontsize = 0.7*fontsize)
if shade:
plane = np.zeros((nX,nY,3))
else:
plane = np.zeros((nX,nY))
# apply shading if True
if shade:
ls = LightSource(azdeg = azdeg, altdeg = altdeg)
rgb = []
# shading can be only used with cBar = 1 or cBar = 2 at the moment
if cBar == 0:
cBar = 1
# check if colormap is set, if not set it to 'copper'
if kwimshow.has_key('cmap') == False:
kwimshow['cmap'] = plt.cm.copper
for i in range(len(data[:,0,0])):
tmp = ls.shade(data[i,:,:], kwimshow['cmap'])
rgb.append(tmp.tolist())
rgb = np.array(rgb)
tmp = []
# calibrate the displayed colors for the data range
image = ax.imshow(plane, vmin=rangeMin, vmax=rangeMax, origin='lower', extent = extent, **kwimshow)
colorbar = fig.colorbar(image)
# change the font size of the colorbar's ytickslabels
cbytick_obj = plt.getp(colorbar.ax.axes, 'yticklabels')
plt.setp(cbytick_obj, fontsize = 0.5*fontsize)
# plot the arrows
# TODO: add some more options
if plotArrows:
# prepare the mash grid where the arrows will be drawn
arrowGridX, arrowGridY = np.meshgrid(np.arange(extent[0], extent[1], float(extent[1]-extent[0])*arrowsRes/len(data[0,:,0])), np.arange(extent[2], extent[3], float(extent[3]-extent[2])*arrowsRes/len(data[0,0,:])))
arrows = ax.quiver(arrowGridX, arrowGridY, arrowsX[0,::arrowsRes,::arrowsRes], arrowsY[0,::arrowsRes,::arrowsRes], units = 'width', pivot = arrowsPivot, width = arrowsWidth, scale = arrowsScale, color = arrowsColor)
# plot the grid for the arrows
if plotArrowsGrid == True:
ax.plot(arrowGridX, arrowGridY, 'k.')
# for real-time image display
if (sloppy == False) or (movieFile):
manager = plt.get_current_fig_manager()
manager.show()
tStep = 0
if movieFile:
movieFiles = []
# start the animation
play('noThread')
# write the movie file
mencodeCommand = "mencoder 'mf://"+movieFile+"*.png' -mf type=png:fps="+np.str(fps)+" -ovc lavc -lavcopts vcodec=mpeg4:vhq:vbitrate="+np.str(bitrate)+" -ffourcc MP4S -oac copy -o "+movieFile+".mpg"
os.system(mencodeCommand)
# clean up the image files
if (keepImages == False):
print 'cleaning up files'
for fname in movieFiles:
os.remove(fname)
else:
# set up the gui
plt.ion()
axPlay = plt.axes([0.1, 0.05, 0.15, 0.05], axisbg='lightgoldenrodyellow')
buttonPlay = plt.Button(axPlay, 'play', color='lightgoldenrodyellow', hovercolor='0.975')
buttonPlay.on_clicked(play_thread)
axPause = plt.axes([0.3, 0.05, 0.15, 0.05], axisbg='lightgoldenrodyellow')
buttonPause = plt.Button(axPause, 'pause', color='lightgoldenrodyellow', hovercolor='0.975')
buttonPause.on_clicked(pausing)
axReverse = plt.axes([0.5, 0.05, 0.15, 0.05], axisbg='lightgoldenrodyellow')
buttonReverse = plt.Button(axReverse, 'reverse', color='lightgoldenrodyellow', hovercolor='0.975')
buttonReverse.on_clicked(reverse)
axForward = plt.axes([0.7, 0.05, 0.15, 0.05], axisbg='lightgoldenrodyellow')
buttonForward = plt.Button(axForward, 'forward', color='lightgoldenrodyellow', hovercolor='0.975')
buttonForward.on_clicked(forward)
# create the time slider
fig.subplots_adjust(bottom=0.2)
sliderTimeAxes = plt.axes([0.2, 0.12, 0.6, 0.03], axisbg='lightgoldenrodyellow')
sliderTime = plt.Slider(sliderTimeAxes, 'time', t[0], t[-1], valinit = 0.0)
def update(val):
global tStep
# find the closest time step to the slider time value
for i in range(len(t)):
if t[i] < sliderTime.val:
tStep = i
if (tStep != len(t)-1):
if (t[tStep+1] - sliderTime.val) < (sliderTime.val - t[tStep]):
tStep += 1
plotFrame()
sliderTime.on_changed(update)
plt.show()
print 'done'
def plotConfusion(confusion, map, grid_spacing, title, file_name, inset=None, fudge=16):
pylab.figure()
axmain = pylab.axes()
gs_x, gs_y = grid_spacing
confusion_cmap_dict = {
'red':(
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
'green':(
(0.0, 0.5, 0.5),
(0.25, 0.5, 0.0),
(0.5, 0.0, 1.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0),
),
'blue':(
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
}
confusion_cmap = LinearSegmentedColormap('confusion', confusion_cmap_dict, 256)
nx, ny = confusion.shape
xs, ys = np.meshgrid( gs_x * np.arange(nx), gs_y * np.arange(ny) )
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=0, vmax=3)
tick_locs = [ 0.375 + 0.75 * l for l in range(4) ]
tick_labels = [ "Correct\nNegative", "False\nAlarm", "Miss", "Hit" ]
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
drawPolitical(map, scale_len=25)
if inset:
lb_y, lb_x = [ b.start for b in inset ]
ub_y, ub_x = [ b.stop + fudge for b in inset ]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp], ys[inset_exp], confusion[inset_exp], cmap=confusion_cmap, vmin=0, vmax=3)
drawPolitical(map)
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
pylab.figure(1,figsize=(22,2))
marker_gene_coverages = parse_midas_data.parse_marker_gene_coverage_distribution(species_name)
max_coverages = []
median_coverages = []
marker_genes = sorted(marker_gene_coverages[marker_gene_coverages.keys()[0]].keys())
for sample in desired_samples:
current_loc = 0
pooled_coverages = []
line, = pylab.plot([-1],[1],'-')
color = pylab.getp(line,'color')
print sample
for gene_idx in xrange(0,len(marker_genes)):
gene_name = marker_genes[gene_idx]
current_loc += 1
pylab.plot([current_loc,current_loc],[1e-09,1e09],'k-')
current_loc += 1
if gene_idx%2==0:
pylab.text(current_loc+10, 900, gene_name, fontsize=6)
else:
pylab.text(current_loc+10, 800, gene_name, fontsize=6)
locs,depths = marker_gene_coverages[sample][gene_name]
def animate_interactive(data,
t=[],
dimOrder=(0, 1, 2),
fps=10.0,
title='',
xlabel='x',
ylabel='y',
fontsize=24,
cBar=0,
sloppy=True,
rangeMin=[],
rangeMax=[],
extent=[-1, 1, -1, 1],
shade=False,
azdeg=0,
altdeg=65,
arrowsX=np.array(0),
arrowsY=np.array(0),
arrowsRes=10,
arrowsPivot='mid',
arrowsWidth=0.002,
arrowsScale=5,
arrowsColor='black',
plotArrowsGrid=False,
movieFile='',
bitrate=1800,
keepImages=False,
figsize=(8, 7),
dpi=None,
**kwimshow):
"""
Assemble a 2D animation from a 3D array.
call signature::
animate_interactive(data, t = [], dimOrder = (0,1,2),
fps = 10.0, title = '', xlabel = 'x', ylabel = 'y',
fontsize = 24, cBar = 0, sloppy = True,
rangeMin = [], rangeMax = [], extent = [-1,1,-1,1],
shade = False, azdeg = 0, altdeg = 65,
arrowsX = np.array(0), arrowsY = np.array(0), arrowsRes = 10,
arrowsPivot = 'mid', arrowsWidth = 0.002, arrowsScale = 5,
arrowsColor = 'black', plotArrowsGrid = False,
movieFile = '', bitrate = 1800, keepImages = False,
figsize = (8, 7), dpi = None,
**kwimshow)
Assemble a 2D animation from a 3D array. *data* has to be a 3D array who's
time index has the same dimension as *t*. The time index of *data* as well
as its x and y indices can be changed via *dimOrder*.
Keyword arguments:
*dimOrder*: [ (i,j,k) ]
Ordering of the dimensions in the data array (t,x,y).
*fps*:
Frames per second of the animation.
*title*:
Title of the plot.
*xlabel*:
Label of the x-axis.
*ylabel*:
Label of the y-axis.
*fontsize*:
Font size of the title, x and y label.
The size of the x- and y-ticks is 0.7*fontsize and the colorbar ticks's
font size is 0.5*fontsize.
*cBar*: [ 0 | 1 | 2 ]
Determines how the colorbar changes:
(0 - no cahnge; 1 - keep extreme values constant; 2 - change extreme values).
*sloppy*: [ True | False ]
If True the update of the plot lags one frame behind. This speeds up the
plotting.
*rangeMin*, *rangeMax*:
Range of the colortable.
*extent*: [ None | scalars (left, right, bottom, top) ]
Data limits for the axes. The default assigns zero-based row,
column indices to the *x*, *y* centers of the pixels.
*shade*: [ False | True ]
If True plot a shaded relief plot instead of the usual colormap.
Note that with this option cmap has to be specified like
cmap = plt.cm.hot instead of cmap = 'hot'. Shading cannot
be used with the cBar = 0 option.
*azdeg*, *altdeg*:
Azimuth and altitude of the light source for the shading.
*arrowsX*:
Data containing the x-component of the arrows.
*arrowsy*:
Data containing the y-component of the arrows.
*arrowsRes*:
Plot every arrowRes arrow.
*arrowsPivot*: [ 'tail' | 'middle' | 'tip' ]
The part of the arrow that is at the grid point; the arrow rotates
about this point.
*arrowsWidth*:
Width of the arrows.
*arrowsScale*:
Scaling of the arrows.
*arrowsColor*:
Color of the arrows.
*plotArrowsGrid*: [ False | True ]
If 'True' the grid where the arrows are aligned to is shown.
*movieFile*: [ None | string ]
The movie file where the animation should be saved to.
If 'None' no movie file is written. Requires 'mencoder' to be installed.
*bitrate*:
Bitrate of the movie file. Set to higher value for higher quality.
*keepImages*: [ False | True ]
If 'True' the images for the movie creation are not deleted.
*figsize*:
Size of the figure in inches.
*dpi*:
Dots per inch of the frame.
**kwimshow:
Remaining arguments are identical to those of pylab.imshow. Refer to that help.
"""
global tStep, sliderTime, pause
# plot the current frame
def plotFrame():
global tStep, sliderTime
if movieFile:
ax.set_title(title + r'$\quad$' + r'$t={0}$'.format(t[tStep]),
fontsize=fontsize)
if shade == False:
image.set_data(data[tStep, :, :])
else:
image.set_data(rgb[tStep, :, :, :])
if (cBar == 0):
pass
if (cBar == 1):
colorbar.set_clim(vmin=data[tStep, :, :].min(),
vmax=data[tStep, :, :].max())
if (cBar == 2):
colorbar.set_clim(vmin=data[tStep, :, :].min(),
vmax=data[tStep, :, :].max())
colorbar.update_bruteforce(data[tStep, :, :])
if plotArrows:
arrows.set_UVC(U=arrowsX[tStep, ::arrowsRes, ::arrowsRes],
V=arrowsY[tStep, ::arrowsRes, ::arrowsRes])
if (sloppy == False) or (movieFile):
manager.canvas.draw()
# play the movie
def play(threadName):
global tStep, sliderTime, pause
pause = False
while (tStep < nT) & (pause == False):
# write the image files for the movie
if movieFile:
plotFrame()
frameName = movieFile + '%06d.png' % tStep
fig.savefig(frameName, dpi=dpi)
movieFiles.append(frameName)
else:
start = time.clock()
# time slider
sliderTime.set_val(t[tStep])
# wait for the next frame (fps)
while (time.clock() - start < 1.0 / fps):
pass # do nothing
tStep += 1
tStep -= 1
# call the play function as a separate thread (for GUI)
def play_thread(event):
global pause
if pause == True:
try:
thread.start_new_thread(play, ("playThread", ))
except:
print "Error: unable to start play thread"
def pausing(event):
global pause
pause = True
def reverse(event):
global tStep, sliderTime
tStep -= 1
if tStep < 0:
tStep = 0
# plot the frame and update the time slider
sliderTime.set_val(t[tStep])
def forward(event):
global tStep, sliderTime
tStep += 1
if tStep > len(t) - 1:
tStep = len(t) - 1
# plot the frame and update the time slider
sliderTime.set_val(t[tStep])
pause = True
plotArrows = False
# check if the data has the right dimensions
if (len(data.shape) != 3 and len(data.shape) != 4):
print 'error: data dimensions are invalid: {0} instead of 3'.format(
len(data.shape))
return -1
# transpose the data according to dimOrder
unOrdered = data
data = np.transpose(unOrdered, dimOrder)
unOrdered = []
# check if arrows should be plotted
if len(arrowsX.shape) == 3:
# transpose the data according to dimOrder
unOrdered = arrowsX
arrowsX = np.transpose(unOrdered, dimOrder)
unOrdered = []
if len(arrowsY.shape) == 3:
# transpose the data according to dimOrder
unOrdered = arrowsY
arrowsY = np.transpose(unOrdered, dimOrder)
unOrdered = []
# check if the dimensions of the arrow arrays match each other
if ((len(arrowsX[:, 0, 0]) != len(arrowsY[:, 0, 0]))
or (len(arrowsX[0, :, 0]) != len(arrowsY[0, :, 0]))
or (len(arrowsX[0, 0, :]) != len(arrowsY[0, 0, :]))):
print 'error: dimensions of arrowX do not match with dimensions of arrowY'
return -1
else:
plotArrows = True
# check if time array has the right length
nT = len(t)
if (nT != len(data[:, 0, 0])):
print 'error: length of time array doesn\'t match length of data array'
return -1
if plotArrows:
if (nT != len(arrowX[:, 0, 0]) or nT != len(arrowX[:, 0, 0])):
print 'error: length of time array doesn\'t match length of arrows array'
return -1
# check if fps is positive
if (fps < 0.0):
print 'error: fps is not positive, fps = {0}'.format(fps)
return -1
# determine the size of the array
nX = len(data[0, :, 0])
nY = len(data[0, 0, :])
# determine the minimum and maximum values of the data set
if not (rangeMin):
rangeMin = np.min(data)
if not (rangeMax):
rangeMax = np.max(data)
# setup the plot
if movieFile:
width = figsize[0]
height = figsize[1]
plt.rc("figure.subplot", bottom=0.15)
plt.rc("figure.subplot", top=0.95)
plt.rc("figure.subplot", right=0.95)
plt.rc("figure.subplot", left=0.15)
fig = plt.figure(figsize=figsize)
ax = plt.axes([0.1, 0.1, .90, .85])
else:
width = figsize[0]
height = figsize[1]
plt.rc("figure.subplot", bottom=0.05)
plt.rc("figure.subplot", top=0.95)
plt.rc("figure.subplot", right=0.95)
plt.rc("figure.subplot", left=0.15)
fig = plt.figure(figsize=figsize)
ax = plt.axes([0.1, 0.25, .85, .70])
ax.set_title(title, fontsize=fontsize)
ax.set_xlabel(xlabel, fontsize=fontsize)
ax.set_ylabel(ylabel, fontsize=fontsize)
plt.xticks(fontsize=0.7 * fontsize)
plt.yticks(fontsize=0.7 * fontsize)
if shade:
plane = np.zeros((nX, nY, 3))
else:
plane = np.zeros((nX, nY))
# apply shading if True
if shade:
ls = LightSource(azdeg=azdeg, altdeg=altdeg)
rgb = []
# shading can be only used with cBar = 1 or cBar = 2 at the moment
if cBar == 0:
cBar = 1
# check if colormap is set, if not set it to 'copper'
if kwimshow.has_key('cmap') == False:
kwimshow['cmap'] = plt.cm.copper
for i in range(len(data[:, 0, 0])):
tmp = ls.shade(data[i, :, :], kwimshow['cmap'])
rgb.append(tmp.tolist())
rgb = np.array(rgb)
tmp = []
# calibrate the displayed colors for the data range
image = ax.imshow(plane,
vmin=rangeMin,
vmax=rangeMax,
origin='lower',
extent=extent,
**kwimshow)
colorbar = fig.colorbar(image)
# change the font size of the colorbar's ytickslabels
cbytick_obj = plt.getp(colorbar.ax.axes, 'yticklabels')
plt.setp(cbytick_obj, fontsize=0.5 * fontsize)
# plot the arrows
# TODO: add some more options
if plotArrows:
# prepare the mash grid where the arrows will be drawn
arrowGridX, arrowGridY = np.meshgrid(
np.arange(
extent[0], extent[1],
float(extent[1] - extent[0]) * arrowsRes / len(data[0, :, 0])),
np.arange(
extent[2], extent[3],
float(extent[3] - extent[2]) * arrowsRes / len(data[0, 0, :])))
arrows = ax.quiver(arrowGridX,
arrowGridY,
arrowsX[0, ::arrowsRes, ::arrowsRes],
arrowsY[0, ::arrowsRes, ::arrowsRes],
units='width',
pivot=arrowsPivot,
width=arrowsWidth,
scale=arrowsScale,
color=arrowsColor)
# plot the grid for the arrows
if plotArrowsGrid == True:
ax.plot(arrowGridX, arrowGridY, 'k.')
# for real-time image display
if (sloppy == False) or (movieFile):
manager = plt.get_current_fig_manager()
manager.show()
tStep = 0
if movieFile:
movieFiles = []
# start the animation
play('noThread')
# write the movie file
mencodeCommand = "mencoder 'mf://" + movieFile + "*.png' -mf type=png:fps=" + np.str(
fps) + " -ovc lavc -lavcopts vcodec=mpeg4:vhq:vbitrate=" + np.str(
bitrate) + " -ffourcc MP4S -oac copy -o " + movieFile + ".mpg"
os.system(mencodeCommand)
# clean up the image files
if (keepImages == False):
print 'cleaning up files'
for fname in movieFiles:
os.remove(fname)
else:
# set up the gui
plt.ion()
axPlay = plt.axes([0.1, 0.05, 0.15, 0.05],
axisbg='lightgoldenrodyellow')
buttonPlay = plt.Button(axPlay,
'play',
color='lightgoldenrodyellow',
hovercolor='0.975')
buttonPlay.on_clicked(play_thread)
axPause = plt.axes([0.3, 0.05, 0.15, 0.05],
axisbg='lightgoldenrodyellow')
buttonPause = plt.Button(axPause,
'pause',
color='lightgoldenrodyellow',
hovercolor='0.975')
buttonPause.on_clicked(pausing)
axReverse = plt.axes([0.5, 0.05, 0.15, 0.05],
axisbg='lightgoldenrodyellow')
buttonReverse = plt.Button(axReverse,
'reverse',
color='lightgoldenrodyellow',
hovercolor='0.975')
buttonReverse.on_clicked(reverse)
axForward = plt.axes([0.7, 0.05, 0.15, 0.05],
axisbg='lightgoldenrodyellow')
buttonForward = plt.Button(axForward,
'forward',
color='lightgoldenrodyellow',
hovercolor='0.975')
buttonForward.on_clicked(forward)
# create the time slider
fig.subplots_adjust(bottom=0.2)
sliderTimeAxes = plt.axes([0.2, 0.12, 0.6, 0.03],
axisbg='lightgoldenrodyellow')
sliderTime = plt.Slider(sliderTimeAxes,
'time',
t[0],
t[-1],
valinit=0.0)
def update(val):
global tStep
# find the closest time step to the slider time value
for i in range(len(t)):
if t[i] < sliderTime.val:
tStep = i
if (tStep != len(t) - 1):
if (t[tStep + 1] - sliderTime.val) < (sliderTime.val -
t[tStep]):
tStep += 1
plotFrame()
sliderTime.on_changed(update)
plt.show()
print 'done'
ncfile = NC(tsfile, "r")
except:
print "ERROR: can't open file %s for reading ..." % tsfile
sys.exit(2)
print " reading 'time' variable ..."
t = ncfile.variables["time"][:] / secpera
n = 3
style = ['b-', 'g-', 'r-']
labels = ['ocean_loss', 'ice_free_land_loss', 'negative_thickness_gain']
for k in range(n):
varname = 'hydro_' + labels[k]
print " reading '%s' variable ..." % varname
var = ncfile.variables[varname][:]
plt.semilogy(t, var / scale, style[k], linewidth=2.5)
plt.hold(True)
ncfile.close()
plt.hold(False)
yy = plt.getp(plt.gca(), 'ylim')
plt.setp(plt.gca(), 'ylim', (yaxismin, yy[1]))
plt.legend(labels, loc=legloc)
plt.xlabel("t (years)", size=16)
plt.ylabel("flux (%s kg/s)" % scalestr, size=16)
plt.grid(True)
print "saving image to file '%s' ..." % outimage
# plt.show()
plt.savefig(outimage, bbox_inches='tight')
def plot_average_oct(self,keys=None,independent=True,string=""):
if independent:
fig = pylab.figure()
average_curves={}
sem_curves={}
curves={}
if keys == None:
keys = self.data_dict.keys()
for k in keys:
xindex, yindex = k
curve = self.data_dict[k]["OCT"]["ORTC"]["data"]
x_values = sorted(curve.keys())
m = numpy.max([curve[l].view()[0][xindex, yindex] for l in x_values])
for curve_label in self.data_dict[k]["OCT"].keys():
if curve_label != 'Contrastsurround = 0%':
orientation = self.data_dict[(xindex,yindex)]["OCT"]["ORTC"]["info"]["pref_or"]
curve = self.data_dict[k]["OCT"][curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[l].view()[0][xindex, yindex] for l in x_values]
x_values=numpy.array(x_values)-orientation
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
inds = numpy.argsort(x_values)
y_values = numpy.take(y_values, inds)
x_values = numpy.take(x_values, inds)
#x_values = sorted(x_values)
if (x_values[0]+numpy.pi/2.0) < 0.0001:
y_values = numpy.append(y_values,[y_values[0]])
x_values = numpy.append(x_values,[numpy.pi/2])
else:
y_values = numpy.append([y_values[-1]],y_values)
x_values = numpy.append([-numpy.pi/2],x_values)
# normalize curve
y_values = y_values / m
if curves.has_key(curve_label):
curves[curve_label].append(numpy.array(y_values))
else:
curves[curve_label]=[numpy.array(y_values)]
for curve_label in curves:
average_curves[curve_label]=numpy.mean(numpy.array(curves[curve_label]),axis=0)
sem_curves[curve_label]=scipy.stats.sem(numpy.array(curves[curve_label]),axis=0)
colors=['red','blue','green','purple','orange','black','yellow']
i=0
for curve_label in average_curves.keys():
pylab.plot(x_values, average_curves[curve_label]*100, lw=3, color=colors[i],label=curve_label)
ax = pylab.gca()
ax.errorbar(x_values, average_curves[curve_label]*100, lw=1, ecolor=colors[i], yerr=sem_curves[curve_label]*100, fmt=None)
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
ax.set_yticks([0,50,100])
ax.set_yticklabels(['0%','50%','100%'])
ax.set_ylim(0,110)
i=i+1
#pylab.legend(loc='lower left')
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels([r'-$\frac{\pi}{2}$',r'0',r'$\frac{\pi}{2}$'], fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
if independent:
release_fig("AverageOCTC" + string)
'axes.labelsize': 11,
'text.fontsize': 11,
'legend.fontsize': 12,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'text.usetex': True,
'figure.figsize': [5,4]}
py.rcParams.update(params)
import numpy as np
import sys
files=['1.txt','2.txt']
for f in files:
data = py.loadtxt(f)
x,y = data.T
p, = py.plot(x[1:], y[1:], 'o-', lw=2, clip_on=False, zorder=10)
c = py.getp(p,'color')
py.plot(x[1], y[1], 'D', clip_on=False, zorder=10, color=c)
py.plot(x[0], y[0], 's', clip_on=False, zorder=10, color=c)
bbox = dict(boxstyle="round", fc="0.9")
arrow = dict(arrowstyle="->")
py.annotate(r'$$x=0, z=0.5$$', xy=(.029, .986), xytext=(.02, .95), bbox=bbox, arrowprops=arrow)
py.annotate(r'$$x=0, z=0.1696$$', xy=(.032, .885), xytext=(.03, .92), bbox=bbox, arrowprops=arrow)
py.xlabel(r'$\mathrm{Kn}$', labelpad=-1)
py.ylabel(r'$T$', y=.8, labelpad=-3, rotation=0)
py.xlim(0,.05)
py.tight_layout()
py.savefig(sys.argv[1])
def correlations_figure(self,raster_plots):
self.fig = pylab.figure(facecolor='w',figsize=(15, 18),dpi=800)
gs = gridspec.GridSpec(3,2)
gs.update(left=0.07, right=0.95, top=0.95, bottom=0.05,wspace=0.2,hspace=0.2)
ax = pylab.subplot(gs[0,0])
m,b = numpy.polyfit(raster_plots["or_suppression"][1],raster_plots["or_suppression"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["or_suppression"][1],raster_plots["or_suppression"][0])
pylab.scatter(raster_plots["or_suppression"][1],raster_plots["or_suppression"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["or_suppression"][1],m*numpy.array(raster_plots["or_suppression"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Orientation-contrast suppression', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[0,1])
m,b = numpy.polyfit(raster_plots["SSI"][1],raster_plots["SSI"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["SSI"][1],raster_plots["SSI"][0])
pylab.scatter(raster_plots["SSI"][1],raster_plots["SSI"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["SSI"][1],m*numpy.array(raster_plots["SSI"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Surround selectivity index', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
print raster_plots.keys()
ax = pylab.subplot(gs[1,0])
m,b = numpy.polyfit(raster_plots["suppresion_index"][1],raster_plots["suppresion_index"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["suppresion_index"][1],raster_plots["suppresion_index"][0])
pylab.scatter(raster_plots["suppresion_index"][1],raster_plots["suppresion_index"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["suppresion_index"][1],m*numpy.array(raster_plots["suppresion_index"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Suppression index', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[1,1])
m,b = numpy.polyfit(raster_plots["counter_suppresion_index"][1],raster_plots["counter_suppresion_index"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["counter_suppresion_index"][1],raster_plots["counter_suppresion_index"][0])
pylab.scatter(raster_plots["counter_suppresion_index"][1],raster_plots["counter_suppresion_index"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["counter_suppresion_index"][1],m*numpy.array(raster_plots["counter_suppresion_index"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Counter suppression index', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[2,0])
pylab.scatter(self.lhi.ravel(),self.MR.ravel()*2,s=3, facecolor = 'r',lw = 0)
#xx,z = running_average(self.lhi.ravel(),self.MR.ravel()*2)
#pylab.plot(xx,z,'k',lw=3.0)
disable_top_right_axis(ax)
pylab.xlabel('Local homogeneity index', fontsize=20)
pylab.ylabel('Modulation ratio', fontsize=20)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[2,1])
pylab.scatter(self.lhi.ravel(),self.OS.ravel(),s=3, facecolor = 'r',lw = 0)
#xx,z = running_average(self.lhi.ravel(),self.OS.ravel())
#pylab.plot(xx,z,'k',lw=3.0)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.xlabel('Local homogeneity index', fontsize=20)
pylab.ylabel('Orientation selectivity', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
print scipy.stats.pearsonr(self.lhi.ravel(),self.OS.ravel())
print scipy.stats.pearsonr(self.lhi.ravel(),self.MR.ravel()*2)
release_fig("CorrelationFigure")
def do_plot(plotfile, component, component2, outFile, log,
minval, maxval, minval2, maxval2, eps, dpi, origin,
annotation, particles, time_ind):
# plotfile plotfile to read data from
# component first variable to plot
# component2 optional second variable to plot
# outFile save plot as "outFile"
# log (= 1,0) plot log (base 10) of the data
# minval (float) specify minimum data range for plot
# maxval (float) specify maximum data range for plot
# eps (= 1,0) make an EPS plot instead of PNG
# dpi (int) (PNG only) make the plot with dpi value
# origin (= 1,0) (3-d only) slice through origin (0,0,0)
# annotation (2-d only) add annotation string under time
# particles (2-d only) particle data
# time_ind array index of time cooresponding to plotfile
#----------------------
# check incoming values:
#-----------------------
if (plotfile == ""):
print "\n---ERROR: plotfile not specified---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if (component == ""):
print "\n---ERROR: no component specified---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if ((log != 1) and (log != 0)):
print "\n---ERROR: invalid value for log (= 1,0)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if ((eps != 1) and (eps != 0)):
print "\n---ERROR: invalid value for eps (= 1,0)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if (minval != None):
try: minval = float(minval)
except ValueError:
print "\n---ERROR: invalid value for minval (= float)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if (maxval != None):
try: maxval = float(maxval)
except ValueError:
print "\n---ERROR: invalid value for maxval (= float)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if (minval2 != None):
try: minval2 = float(minval2)
except ValueError:
print "\n---ERROR: invalid value for minval2 (= float)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if (maxval2 != None):
try: maxval2 = float(maxval2)
except ValueError:
print "\n---ERROR: invalid value for maxval2 (= float)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if ((origin != 1) and (origin != 0)):
print "\n---ERROR: invalid value for origin (= 1,0)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
if (dpi != None):
try: dpi = int(dpi)
except ValueError:
print "\n---ERROR: invalid value for dpi (= int)---"
print " (plotsinglevar_parts.py)\n"
usage()
sys.exit(2)
#--------------------------------------------------------------------------
# construct the output file name
#--------------------------------------------------------------------------
if (outFile == ""):
outFile = os.path.normpath(plotfile) + "_" + component
if (not component2 == ""): outFile += "_" + component2
if (not eps):
outFile += ".png"
else:
outFile += ".eps"
else:
# make sure the proper extension is used
if (not eps):
if (not string.rfind(outFile, ".png") > 0):
outFile = outFile + ".png"
else:
if (not string.rfind(outFile, ".eps") > 0):
outFile = outFile + ".eps"
#--------------------------------------------------------------------------
# read in the meta-data from the plotfile
#--------------------------------------------------------------------------
(nx, ny, nz) = fsnapshot.fplotfile_get_size(plotfile)
time = fsnapshot.fplotfile_get_time(plotfile)
(xmin, xmax, ymin, ymax, zmin, zmax) = \
fsnapshot.fplotfile_get_limits(plotfile)
x = xmin + numpy.arange( (nx), dtype=numpy.float64 )*(xmax - xmin)/nx
y = ymin + numpy.arange( (ny), dtype=numpy.float64 )*(ymax - ymin)/ny
if (nz > 0):
z = zmin + numpy.arange( (nz), dtype=numpy.float64 )*(zmax - zmin)/nz
if (nz == -1):
#----------------------------------------------------------------------
# 2-d plots
#----------------------------------------------------------------------
extent = xmin, xmax, ymin, ymax
# read in the main component
data = numpy.zeros( (nx, ny), dtype=numpy.float64)
(data, err) = fsnapshot.fplotfile_get_data_2d(plotfile, component, data)
if (not err == 0):
sys.exit(2)
data = numpy.transpose(data)
if (minval == None): minval = numpy.min(data)
if (maxval == None): maxval = numpy.max(data)
# read in the component #2, if present
if (not component2 == ""):
data2 = numpy.zeros( (nx, ny), dtype=numpy.float64)
(data2, err) = fsnapshot.fplotfile_get_data_2d(plotfile, component2, data2)
if (not err == 0):
sys.exit(2)
data2 = numpy.transpose(data2)
if (minval2 == None): minval2 = numpy.min(data2)
if (maxval2 == None): maxval2 = numpy.max(data2)
if log:
data = numpy.log10(data)
if (not component2 == ""):
data2 = numpy.log10(data2)
minval2 = math.log10(minval2)
maxval2 = math.log10(maxval2)
minval = math.log10(minval)
maxval = math.log10(maxval)
#----------------------------------------------------------------------
# plot main component
#----------------------------------------------------------------------
if (not component2 == ""):
ax = pylab.subplot(1,2,1)
pylab.subplots_adjust(wspace=0.5)
else:
ax = pylab.subplot(1,1,1)
divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)
im=pylab.imshow(data,origin='lower', extent=extent, vmin=minval, vmax=maxval)
#----------------------------------------------------------------------
# plot the particle data
#----------------------------------------------------------------------
n=0
while(n < len(particles)):
# sometimes the length of particle history is larger than index
if (time_ind < len(particles[n].history)):
pylab.scatter(particles[n].history[time_ind].xyz[0],
particles[n].history[time_ind].xyz[1],
s=0.5,c='black',marker='o')
n += 1
pylab.title(component)
pylab.xlabel("x")
pylab.ylabel("y")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
# make space for a colorbar -- getting it the same size as the
# vertical extent of the plot is surprisingly tricky. See
# http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#colorbar-whose-height-or-width-in-sync-with-the-master-axes
ax_cb = divider.new_horizontal(size="10%", pad=0.1)
fig1 = ax.get_figure()
fig1.add_axes(ax_cb)
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(im, format=formatter, cax=ax_cb)
# make the font size for the colorbar axis labels small. Note
#----------------------------------------------------------------------
# plot component #2
#----------------------------------------------------------------------
if (not component2 == ""):
ax = pylab.subplot(1,2,2)
divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)
im = pylab.imshow(data2, origin='lower', extent=extent,
vmin=minval2, vmax=maxval2)
#----------------------------------------------------------------
# plot the particle data
#----------------------------------------------------------------
n=0
while(n < len(particles)):
# sometimes the length of particle history is larger than index
if (time_ind < len(particles[n].history)):
pylab.scatter(particles[n].history[time_ind].xyz[0],
particles[n].history[time_ind].xyz[1],
s=0.5,c='black',marker='o')
n += 1
pylab.title(component2)
pylab.xlabel("x")
pylab.ylabel("y")
# axis labels in scientific notation with LaTeX
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
# make space for a colorbar -- getting it the same size as
# the vertical extent of the plot is surprisingly tricky.
# See
# http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#colorbar-whose-height-or-width-in-sync-with-the-master-axes
ax_cb = divider.new_horizontal(size="10%", pad=0.1)
fig1 = ax.get_figure()
fig1.add_axes(ax_cb)
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(im, format=formatter, cax=ax_cb)
# make the font size for the colorbar axis labels small. Note the
# offsetText is the 10^N that appears at the top of the y-axis.
cl = pylab.getp(cb.ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cb.ax.yaxis.offsetText.set_fontsize("small")
#ax_cb.yaxis.tick_right()
else:
#----------------------------------------------------------------------
# 3-d plot
#----------------------------------------------------------------------
###################################
# NOT SUPPORTED YET
###################################
print "\n\n--- ERROR: 3-d not yet implemented ---"
print " (plotsinglevar_parts.py)\n"
sys.exit(2)
# figure out the maximum width -- we will plot xy, xz, and yz
w1 = xmax - xmin # also w2
w3 = ymax - ymin
if (w3 > w1):
scale = w3
else:
scale = w1
# starting points for the figure positions
# assume that the width of the plotting area is 0.05 to 0.95,
# leaving 0.9 to be split amongst the 3 plots. So each has a
# width of 0.3
# for the height, we will assume that the colorbar at the
# bottom gets 0.15, and that we go until 0.95, leaving 0.8 of
# height for the plots.
pos1 = [0.05, 0.15, 0.3, 0.8]
pos2 = [0.35, 0.15, 0.3, 0.8]
pos3 = [0.65, 0.15, 0.3, 0.8]
fig = pylab.figure()
# x-y
extent = xmin, xmax, ymin, ymax
# read in the main component
data = numpy.zeros( (nx, ny), dtype=numpy.float64)
indir = 3
(data, err) = \
fsnapshot.fplotfile_get_data_3d(plotfile, component, indir, origin, data)
if (not err == 0):
sys.exit(2)
data = numpy.transpose(data)
if log:
data = numpy.log10(data)
if (not minval == None): minval = math.log10(minval)
if (not maxval == None): maxval = math.log10(maxval)
ax = pylab.subplot(1,3,1)
pylab.subplots_adjust(wspace=0.4)
#fig.add_axes(pos1)
im=pylab.imshow(data,origin='lower', extent=extent,
vmin=minval, vmax=maxval, axes=pos1)
pylab.xlabel("x")
pylab.ylabel("y")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.xaxis.offsetText.set_fontsize("small")
ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cl = pylab.getp(ax, 'xmajorticklabels')
pylab.setp(cl, fontsize=10)
# do a fixed offset in pixels from the (xmin,ymin) data point
fig1 = ax.get_figure()
trans=matplotlib.transforms.offset_copy(ax.transData, x=0, y=-0.5,
fig=fig1, units='inches')
pylab.text(xmin, ymin, "time = %7.3g s" % (time),
verticalalignment="bottom", transform = trans,
clip_on=False, fontsize=10)
# x-z
extent = xmin, xmax, zmin, zmax
# read in the main component
data = numpy.zeros( (nx, nz), dtype=numpy.float64)
(data, err) = \
fsnapshot.fplotfile_get_data_3d(plotfile, component, 2, origin, data)
if (not err == 0):
sys.exit(2)
data = numpy.transpose(data)
if log:
data = numpy.log10(data)
if (not minval == None): minval = math.log10(minval)
if (not maxval == None): maxval = math.log10(maxval)
ax = pylab.subplot(1,3,2)
#fig.add_axes(pos2)
im=pylab.imshow(data,origin='lower', extent=extent,
vmin=minval, vmax=maxval, axes=pos2)
pylab.xlabel("x")
pylab.ylabel("z")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.xaxis.offsetText.set_fontsize("small")
ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cl = pylab.getp(ax, 'xmajorticklabels')
pylab.setp(cl, fontsize=10)
# y-z
extent = ymin, ymax, zmin, zmax
# read in the main component
data = numpy.zeros( (ny, nz), dtype=numpy.float64)
(data, err) = \
fsnapshot.fplotfile_get_data_3d(plotfile, component, 1, origin, data)
if (not err == 0):
sys.exit(2)
data = numpy.transpose(data)
if log:
data = numpy.log10(data)
if (not minval == None): minval = math.log10(minval)
if (not maxval == None): maxval = math.log10(maxval)
ax = pylab.subplot(1,3,3)
#fig.add_axes(pos3)
im=pylab.imshow(data,origin='lower', extent=extent,
vmin=minval, vmax=maxval, axes=pos3)
pylab.xlabel("y")
pylab.ylabel("z")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.xaxis.offsetText.set_fontsize("small")
ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cl = pylab.getp(ax, 'xmajorticklabels')
pylab.setp(cl, fontsize=10)
# colorbar
pylab.subplots_adjust(bottom=0.1, left=0.05, right=0.95)
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cax = pylab.axes([0.05, 0.06, 0.9, 0.04])
pylab.colorbar(orientation="horizontal", cax=cax, format=formatter)
pylab.title(component)
#--------------------------------------------------------------------------
# save the figure
#--------------------------------------------------------------------------
if (not eps):
pylab.savefig(outFile, bbox_inches='tight', dpi=dpi, pad_inches=0.33)
else:
pylab.savefig(outFile, bbox_inches='tight', pad_inches=0.33)
def plot_orientation_contrast_tuning(self, xindex, yindex,string,independent=True):
if independent:
fig = pylab.figure()
colors=['red','blue','green','purple','orange','black','yellow']
orientation = self.data_dict[(xindex,yindex)]["OCT"]["ORTC"]["info"]["pref_or"]
measurment = self.data_dict[(xindex,yindex)]["OCT"]
i = 0
m = 0
for curve_label in measurment.keys():
#if curve_label != 'Contrastsurround = 100%':
curve = measurment[curve_label]["data"]
x_values = sorted(curve.keys())
y_values = []
for k in x_values:
y_values.append(curve[k].view()[0][xindex, yindex])
if curve_label != 'ORTC':
z = []
ks = sorted(measurment["ORTC"]["data"].keys())
for k in ks:
z.append(measurment["ORTC"]["data"][k].view()[0][xindex, yindex])
ssi = self.calculate_octc_selectivity(2*x_values,numpy.max(z)-y_values)
self.data_dict[(xindex,yindex)]["OCT"][curve_label]["measures"]["SSI"]=ssi
#self.data_dict[(xindex,yindex)]["OCT"]['Contrastsurround = 100%']["measures"]["SSI"]=ssi
x_values=numpy.array(x_values)-orientation
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
inds = numpy.argsort(x_values)
y_values = numpy.take(y_values, inds)
x_values = sorted(x_values)
#numpy.append(y_values,y_values[0])
#numpy.append(x_values,x_values[0]+numpy.pi)
if x_values[0] == -numpy.pi/2:
y_values = numpy.append(y_values,[y_values[0]])
x_values = numpy.append(x_values,[numpy.pi/2])
else:
y_values = numpy.append([y_values[-1]],y_values)
x_values = numpy.append([-numpy.pi/2],x_values)
m = max(m,max(y_values))
pylab.plot(x_values, y_values, lw=3, color=colors[i],label=curve_label)
i+=1
disable_top_right_axis(pylab.gca())
ax = pylab.gca()
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
#ax.set_xticklabels([r'-$\frac{\mathrm{\mathsf{\pi}}{2}$',r'0',r'$\frac{\mathrm{\mathsf{\pi}}{2}$'], fontProperties)
# get ceil at first decimal point
m = numpy.ceil(m)
ax.set_yticks([0.1,m/2,m])
ax.set_yticklabels(ax.get_yticks(), fontProperties)
#ax.set_ylim(0.1,m)
#pylab.show()
if independent:
release_fig("OCTC[" + str(xindex) + "," + str(yindex) + "] " + string)
change_axis.text(species_idx,
-len(gene_changes) - 3,
'*',
fontsize=4)
m = change_axis.scatter([200], [1],
c=[0],
vmin=0,
vmax=vmax,
cmap=cmap,
marker='^')
cbar = fig.colorbar(m, cax=cax, orientation='vertical', ticks=[0, 1, 2, 3])
cbar.set_ticklabels(['$1$', '$10$', '$10^{2}$', '$10^{3}$'])
cbar.set_label('Number of changes', rotation=270, labelpad=10)
cl = pylab.getp(cbar.ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=9)
#fig.text(0.945,0.05,'$\\pi/\\pi_0$',fontsize=12)
cbar.ax.tick_params(labelsize=5)
change_axis.text(20, 25, 'SNVs', fontsize=5)
change_axis.text(20, -20, 'Genes', fontsize=5)
######
#
# Distribution of nucleotide changes
#
######
pooled_snp_change_distribution = numpy.array(pooled_snp_change_distribution)
pooled_between_snp_change_distribution = numpy.array(
sys.stderr.write("Calculating total fixation matrix...\n")
fixation_matrix_all, fixation_opportunities_all = diversity_utils.calculate_fixation_matrix(allele_counts_map, passed_sites_map, min_change=min_change)
sys.stderr.write("Done!\n")
# Calculate fraction nonsynonymous
dN = fixation_matrix_non/fixation_opportunities_non
dS = fixation_matrix_syn/fixation_opportunities_syn
dNplusdS = (dN+dS)
fraction_nonsynonymous = dN/(dNplusdS+(dNplusdS==0))
# Calculate total divergence
dtot = fixation_matrix_all/fixation_opportunities_all
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
same_sample_idxs, same_subject_idxs, diff_subject_idxs = parse_midas_data.calculate_subject_pairs(subject_sample_map, desired_samples)
line, = main_axis.semilogx(dtot[diff_subject_idxs], fraction_nonsynonymous[diff_subject_idxs],'.',markersize=2,alpha=0.5,markeredgewidth=0.0)
colorVal = pylab.getp(line,'color')
main_axis.semilogx(dtot[same_subject_idxs], fraction_nonsynonymous[same_subject_idxs],'s',alpha=0.5,markersize=2,color=colorVal,markeredgewidth=0.0)
legend_axis.plot([-2,-1],[-2,-1],'.',markersize=3,alpha=0.5,markeredgewidth=0.0, label=species_label)
legend_axis.legend(loc='upper center',frameon=False,fontsize=5,numpoints=1,ncol=1,handlelength=1)
pylab.savefig('%s/%s_genus_dNdS_vs_dS.pdf' % (parse_midas_data.analysis_directory,genus_name),bbox_inches='tight')
pylab.savefig('%s/%s_genus_dNdS_vs_dS.png' % (parse_midas_data.analysis_directory,genus_name),bbox_inches='tight')
mutator_mut_axis.set_xlim([-1000,61000])
mutator_mut_axis.set_xticks([0,60000])
mutator_mut_axis.set_xticklabels(['0','60k'])
mutator_mut_axis.set_ylim([0,3000])
mutator_mut_axis.set_yticks([0,3000])
mutator_mut_axis.set_yticklabels(['0','3k'])
mutator_mut_axis.tick_params(axis='y',length=3,pad=1,labelsize=5)
mutator_mut_axis.tick_params(axis='x', length=3,pad=1,labelsize=5)
mutator_mut_axis.fill_between(numpy.array([0,61000]),numpy.array([0,0]), numpy.array([3000,3000]),color='w',zorder=20)
cl = pylab.getp(mutator_mut_axis, 'xmajorticklabels')
pylab.setp(cl, fontsize=5)
cl = pylab.getp(mutator_mut_axis, 'ymajorticklabels')
pylab.setp(cl, fontsize=5)
mutator_mut_axis.get_yaxis().tick_left()
mutator_mut_axis.get_xaxis().tick_bottom()
[i.set_linewidth(0.75) for i in mutator_mut_axis.spines.itervalues()]
# Moved these down so that it will work better?
mut_axis.tick_params(axis='y', direction='out',length=3,pad=1,labelsize=5)
mut_axis.tick_params(axis='x', direction='out',length=3,pad=1,labelsize=5)
mut_axis.set_ylabel('Mutations, $M(t)$ ',labelpad=-2)
#############################
#
def plotConfusion(confusion, grid, title, file_name, inset=None, fudge=16):
pylab.figure()
axmain = pylab.axes()
confusion_cmap_dict = {
'red':(
(0.0, 1.0, 1.0),
(0.2, 1.0, 0.5), # Missing to Correct Negative
(0.4, 0.5, 1.0), # Correct Negative to False Alarm
(0.6, 1.0, 1.0), # False Alarm to Miss
(0.8, 1.0, 0.0), # Miss to Hit
(1.0, 0.0, 0.0),
),
'green':(
(0.0, 1.0, 1.0),
(0.2, 1.0, 0.5),
(0.4, 0.5, 0.0),
(0.6, 0.0, 1.0),
(0.8, 1.0, 0.5),
(1.0, 0.5, 0.5),
),
'blue':(
(0.0, 1.0, 1.0),
(0.2, 1.0, 0.5),
(0.4, 0.5, 1.0),
(0.6, 1.0, 0.0),
(0.8, 0.0, 0.0),
(1.0, 0.0, 0.0),
),
}
confusion_cmap = LinearSegmentedColormap('confusion', confusion_cmap_dict, 256)
tick_labels = [ "Missing", "Correct\nNegative", "False\nAlarm", "Miss", "Hit" ]
min_label = -1
xs, ys = grid.getXY()
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
tick_locs = np.linspace(-1, min_label + len(tick_labels) - 2, len(tick_labels))
tick_locs += (tick_locs[1] - tick_locs[0]) / 2
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
grid.drawPolitical()
if inset:
lb_y, lb_x = [ b.start for b in inset ]
ub_y, ub_x = [ b.stop + fudge for b in inset ]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp], ys[inset_exp], confusion[inset_exp], cmap=confusion_cmap, vmin=0, vmax=3)
grid.drawPolitical()
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plot(self, labels=None, numbers=False, origin='upper',
numbers_alpha=None, xlabels_vertical=True, numbers_kwargs={},
**kwargs):
"""Provide presentation of confusion matrix in image
Parameters
----------
labels : list of int or str
Optionally provided labels guarantee the order of
presentation. Also value of None places empty column/row,
thus provides visual groupping of labels (Thanks Ingo)
numbers : bool
Place values inside of confusion matrix elements
numbers_alpha : None or float
Controls textual output of numbers. If None -- all numbers
are plotted in the same intensity. If some float -- it controls
alpha level -- higher value would give higher contrast. (good
value is 2)
origin : str
Which left corner diagonal should start
xlabels_vertical : bool
Either to plot xlabels vertical (benefitial if number of labels
is large)
numbers_kwargs : dict
Additional keyword parameters to be added to numbers (if numbers
is True)
**kwargs
Additional arguments given to imshow (\eg me cmap)
Returns
-------
(fig, im, cb) -- figure, imshow, colorbar
"""
externals.exists("pylab", raise_=True)
import pylab as pl
self.compute()
labels_order = labels
# some shortcuts
labels = self.__labels
labels_map = self.__labels_map
labels_map_rev = self.__labels_map_rev
matrix = self.__matrix
# craft original mapping from a label into index in the matrix
labels_indexes = dict([(x,i) for i,x in enumerate(labels)])
labels_rev = []
if labels_map_rev is not None:
labels_rev = [','.join([str(x) for x in labels_map_rev[l]])
for l in labels]
labels_map_full = dict(zip(labels_rev, labels))
if labels_order is not None:
labels_order_filtered = filter(lambda x:x is not None, labels_order)
labels_order_filtered_set = set(labels_order_filtered)
# Verify if all labels provided in labels
if set(labels) == labels_order_filtered_set:
# We were provided numerical (most probably) set
labels_plot = labels_order
elif len(labels_rev) \
and set(labels_rev) == labels_order_filtered_set:
# not clear if right whenever there were multiple labels
# mapped into the same
labels_plot = []
for l in labels_order:
v = None
if l is not None: v = labels_map_full[l]
labels_plot += [v]
else:
raise ValueError, \
"Provided labels %s do not match set of known " \
"original labels (%s) or mapped labels (%s)" % \
(labels_order, labels, labels_rev)
else:
labels_plot = labels
# where we have Nones?
isempty = np.array([l is None for l in labels_plot])
non_empty = np.where(np.logical_not(isempty))[0]
# numbers of different entries
NlabelsNN = len(non_empty)
Nlabels = len(labels_plot)
if matrix.shape != (NlabelsNN, NlabelsNN):
raise ValueError, \
"Number of labels %d doesn't correspond the size" + \
" of a confusion matrix %s" % (NlabelsNN, matrix.shape)
confusionmatrix = np.zeros((Nlabels, Nlabels))
mask = confusionmatrix.copy()
ticks = []
tick_labels = []
# populate in a silly way
reordered_indexes = [labels_indexes[i] for i in labels_plot
if i is not None]
for i, l in enumerate(labels_plot):
if l is not None:
j = labels_indexes[l]
confusionmatrix[i, non_empty] = matrix[j, reordered_indexes]
confusionmatrix[non_empty, i] = matrix[reordered_indexes, j]
ticks += [i + 0.5]
if labels_map_rev is not None:
tick_labels += ['/'.join(labels_map_rev[l])]
else:
tick_labels += [str(l)]
else:
mask[i, :] = mask[:, i] = 1
confusionmatrix = np.ma.MaskedArray(confusionmatrix, mask=mask)
# turn off automatic update if interactive
if pl.matplotlib.get_backend() == 'TkAgg':
pl.ioff()
fig = pl.gcf()
ax = pl.gca()
ax.axis('off')
# some customization depending on the origin
xticks_position, yticks, ybottom = {
'upper': ('top', [Nlabels-x for x in ticks], 0.1),
'lower': ('bottom', ticks, 0.2)
}[origin]
# Plot
axi = fig.add_axes([0.15, ybottom, 0.7, 0.7])
im = axi.imshow(confusionmatrix, interpolation="nearest", origin=origin,
aspect='equal', extent=(0, Nlabels, 0, Nlabels),
**kwargs)
# plot numbers
if numbers:
numbers_kwargs_ = {'fontsize': 10,
'horizontalalignment': 'center',
'verticalalignment': 'center'}
maxv = float(np.max(confusionmatrix))
colors = [im.to_rgba(0), im.to_rgba(maxv)]
for i,j in zip(*np.logical_not(mask).nonzero()):
v = confusionmatrix[j, i]
# scale alpha non-linearly
if numbers_alpha is None:
alpha = 1.0
else:
# scale according to value
alpha = 1 - np.array(1 - v / maxv) ** numbers_alpha
y = {'lower':j, 'upper':Nlabels-j-1}[origin]
numbers_kwargs_['color'] = colors[int(v<maxv/2)]
numbers_kwargs_.update(numbers_kwargs)
pl.text(i+0.5, y+0.5, '%d' % v, alpha=alpha, **numbers_kwargs_)
maxv = np.max(confusionmatrix)
boundaries = np.linspace(0, maxv, np.min((maxv, 10)), True)
# Label axes
pl.xlabel("targets")
pl.ylabel("predictions")
pl.setp(axi, xticks=ticks, yticks=yticks,
xticklabels=tick_labels, yticklabels=tick_labels)
axi.xaxis.set_ticks_position(xticks_position)
axi.xaxis.set_label_position(xticks_position)
if xlabels_vertical:
pl.setp(pl.getp(axi, 'xticklabels'), rotation='vertical')
axcb = fig.add_axes([0.8, ybottom, 0.02, 0.7])
cb = pl.colorbar(im, cax=axcb, format='%d', ticks = boundaries)
if pl.matplotlib.get_backend() == 'TkAgg':
pl.ion()
pl.draw()
# Store it primarily for testing
self._plotted_confusionmatrix = confusionmatrix
return fig, im, cb
def Figure6(self):
self.fig = pylab.figure(facecolor='w',figsize=(14, 12),dpi=800)
gs = gridspec.GridSpec(57, 36)
gs.update(left=0.05, right=0.95, top=0.95, bottom=0.05,wspace=0.2,hspace=0.2)
picked_stcs = [(57,57), (55,51) , (49,51) , (47,55) , (53,59) , (47,61)]
ax = pylab.subplot(gs[1:19,9:27])
self.plot_average_size_tuning_curve(independent=False)
pylab.ylabel('Normalized response', fontsize=20)
ax = pylab.subplot(gs[22:32,1:11])
self.plot_size_tunning(picked_stcs[0][0], picked_stcs[0][1],independent=False)
remove_x_tick_labels()
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[22:32,13:23])
self.plot_size_tunning(picked_stcs[1][0], picked_stcs[1][1],independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[22:32,25:35])
self.plot_size_tunning(picked_stcs[2][0], picked_stcs[2][1],independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,1:11])
self.plot_size_tunning(picked_stcs[3][0], picked_stcs[3][1],independent=False)
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[34:44,13:23])
self.plot_size_tunning(picked_stcs[4][0], picked_stcs[4][1],independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,25:35])
self.plot_size_tunning(picked_stcs[5][0], picked_stcs[5][1],independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
# PLOT EXPERIMENTAL DATA
x,y = zip(*[(-0.0118, 10.3038),(0.0398, 5.0943),(0.1025, 4.8631),(0.2222, 10.5367),(0.3872, 22.694),(0.6212, 21.1903),(0.9463, 25.9388),(1.4487, 16.2168),(2.219, 17.9579),(3.3435, 9.6286),(4.9921, 21.2156)])
ox,oy = zip(*[(0.0447, 25.4706),(0.1181, 49.6678),(0.2268, 41.2169),(0.3803, 55.4581),(0.6206, 39.3668),(0.963, 36.5901),(1.4654, 29.8782),(2.2245, 23.9781),(3.3429, 25.1424),(4.9977, 23.7626)])
x = numpy.array(x)
y = numpy.array(y)
ox = numpy.array(ox)
oy = numpy.array(oy)
ax = pylab.subplot(gs[47:57,1:11])
ax.plot(x,y,lw=3,color='r')
pylab.hold('on')
ax.plot(ox,oy,lw=3,color='b')
ax.set_xlim(0,5)
ax.set_xticks([0,1,2,3,4,5])
ax.set_ylim(0,80)
ax.set_yticks([0,40,80])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
lc_x,lc_y = zip(*[(-0.0487, -0.0419),(1.3979, 6.7485),(2.0126, 8.1943),(2.8208, 10.3224),(3.9922, 4.6368),(5.6557, 3.5735),(7.947, 5.1138),(11.0084, 4.837)])
mc_x,mc_y = zip(*[(-0.0487, -0.0419),(1.347, 13.4836),(1.9927, 15.4864),(2.7962, 13.3683),(4.0149, 8.2393),(5.6128, 7.3489),(7.9576, 7.1724),(11.0201, 6.5096)])
hc_x,hc_y = zip(*[(-0.0487, -0.0419),(1.3515, 17.7727),(1.9832, 12.9988),(2.8384, 9.8502),(4.0052, 5.8375),(5.6352, 11.0801),(7.9759, 12.4049),(10.9943, 9.9844)])
lc_x = numpy.array(lc_x)
lc_y = numpy.array(lc_y)
mc_x = numpy.array(mc_x)
mc_y = numpy.array(mc_y)
hc_x = numpy.array(hc_x)
hc_y = numpy.array(hc_y)
ax = pylab.subplot(gs[47:57,13:23])
ax.plot(lc_x,lc_y,lw=3,color='r')
ax.plot(lc_x,mc_y,lw=3,color='g')
ax.plot(lc_x,hc_y,lw=3,color='b')
ax.set_xlim(0,12)
ax.set_xticks([0,5,10])
ax.set_ylim(0,20)
ax.set_yticks([0,10,20])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
lc_x,lc_y = zip(*[(0.665, 14.9452),(1.0443, 22.295),(1.5573, 27.5117),(2.2633, 32.0173),(3.3754, 37.7079),(5.2272, 28.233),(7.5956, 30.1319),(11.9241, 32.2685),(17.7866, 40.8027),(26.8654, 43.6499)])
mc_x,mc_y = zip(*[(0.6909, 11.628),(1.0305, 15.8968),(1.5377, 30.3552),(2.2968, 60.2165),(3.5141, 63.0637),(5.2352, 51.9297),(8.1111, 49.5638),(12.0944, 52.4108),(18.0381, 58.8123),(27.2453, 61.6595)])
hc_x,hc_y = zip(*[(0.6827, 25.3719),(1.0312, 27.745),(1.5594, 48.6018),(2.3, 82.0174),(3.5202, 89.604),(5.3143, 84.6312),(8.0208, 75.867),(12.1168, 81.0838),(18.5382, 83.4571),(27.2836, 83.4604)])
lc_x = numpy.array(lc_x)
lc_y = numpy.array(lc_y)
mc_x = numpy.array(mc_x)
mc_y = numpy.array(mc_y)
hc_x = numpy.array(hc_x)
hc_y = numpy.array(hc_y)
ax = pylab.subplot(gs[47:57,25:35])
ax.semilogx(lc_x,lc_y,lw=3,color='r')
ax.semilogx(lc_x,mc_y,lw=3,color='g')
ax.semilogx(lc_x,hc_y,lw=3,color='b')
ax.set_xlim(0.6,30)
ax.set_xticks([0.5,1.0,3.0,10,50])
ax.set_ylim(0,100)
ax.set_yticks([0.0,50.0,100])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
release_fig("Figure6")
def do_plot(plotfile, component, component2, outFile, log, minval, maxval,
minval2, maxval2, eps, dpi, origin, annotation, xmin_pass,
ymin_pass, zmin_pass, xmax_pass, ymax_pass, zmax_pass):
pylab.rc("font", size=9)
#--------------------------------------------------------------------------
# construct the output file name
#--------------------------------------------------------------------------
if (outFile == ""):
outFile = os.path.normpath(plotfile) + "_" + component
if (not component2 == ""): outFile += "_" + component2
if (not eps):
outFile += ".png"
else:
outFile += ".eps"
else:
# make sure the proper extension is used
if (not eps):
if (not string.rfind(outFile, ".png") > 0):
outFile = outFile + ".png"
else:
if (not string.rfind(outFile, ".eps") > 0):
outFile = outFile + ".eps"
#--------------------------------------------------------------------------
# read in the meta-data from the plotfile
#--------------------------------------------------------------------------
(nx, ny, nz) = fsnapshot.fplotfile_get_size(plotfile)
time = fsnapshot.fplotfile_get_time(plotfile)
(xmin, xmax, ymin, ymax, zmin, zmax) = \
fsnapshot.fplotfile_get_limits(plotfile)
dx = (xmax - xmin) / nx
x = xmin + numpy.arange((nx), dtype=numpy.float64) * dx
dy = (ymax - ymin) / ny
y = ymin + numpy.arange((ny), dtype=numpy.float64) * dy
if (nz > 0):
dz = (zmax - zmin) / nz
z = zmin + numpy.arange((nz), dtype=numpy.float64) * dz
if (nz == -1):
#----------------------------------------------------------------------
# 2-d plots
#----------------------------------------------------------------------
extent = [xmin, xmax, ymin, ymax]
ix0 = 0
ix = nx
iy0 = 0
iy = ny
if (not xmin_pass == None):
extent[0] = xmin_pass
ix0 = int((xmin_pass - xmin) / dx)
if (not ymin_pass == None):
extent[2] = ymin_pass
iy0 = int((ymin_pass - ymin) / dy)
if (not xmax_pass == None):
extent[1] = xmax_pass
ix = int((xmax_pass - xmin) / dx)
if (not ymax_pass == None):
extent[3] = ymax_pass
iy = int((ymax_pass - ymin) / dy)
sparseX = 0
if (ny >= 3 * nx):
sparseX = 1
# read in the main component
data = numpy.zeros((nx, ny), dtype=numpy.float64)
(data, err) = fsnapshot.fplotfile_get_data_2d(plotfile, component,
data)
if (not err == 0):
sys.exit(2)
data = numpy.transpose(data)
# read in the component #2, if present
if (not component2 == ""):
data2 = numpy.zeros((nx, ny), dtype=numpy.float64)
(data2,
err) = fsnapshot.fplotfile_get_data_2d(plotfile, component2,
data2)
if (not err == 0):
sys.exit(2)
data2 = numpy.transpose(data2)
# log?
if log:
if (numpy.min(data) < 0):
data = numpy.log10(numpy.abs(data))
else:
data = numpy.log10(data)
if (not component2 == ""):
if (numpy.min(data2) < 0.0):
data2 = numpy.log10(numpy.abs(data2))
else:
data2 = numpy.log10(data2)
if (not minval == None): minval = math.log10(minval)
if (not maxval == None): maxval = math.log10(maxval)
if (not minval2 == None): minval2 = math.log10(minval2)
if (not maxval2 == None): maxval2 = math.log10(maxval2)
#----------------------------------------------------------------------
# plot main component
#----------------------------------------------------------------------
if (not component2 == ""):
ax = pylab.subplot(1, 2, 1)
pylab.subplots_adjust(wspace=0.5)
else:
ax = pylab.subplot(1, 1, 1)
divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)
im = pylab.imshow(data[iy0:iy, ix0:ix],
origin='lower',
extent=extent,
vmin=minval,
vmax=maxval)
pylab.title(component)
pylab.xlabel("x")
pylab.ylabel("y")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
if (sparseX):
ax.xaxis.set_major_locator(pylab.MaxNLocator(3))
# make space for a colorbar -- getting it the same size as the
# vertical extent of the plot is surprisingly tricky. See
# http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#colorbar-whose-height-or-width-in-sync-with-the-master-axes
ax_cb = divider.new_horizontal(size="10%", pad=0.1)
fig1 = ax.get_figure()
fig1.add_axes(ax_cb)
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(im, format=formatter, cax=ax_cb)
# make the font size for the colorbar axis labels small. Note
# the offsetText is the 10^N that appears at the top of the
# y-axis.
cl = pylab.getp(cb.ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cb.ax.yaxis.offsetText.set_fontsize("small")
# do a fixed offset in pixels from the (xmin,ymin) data point
trans = matplotlib.transforms.offset_copy(ax.transData,
x=0,
y=-0.5,
fig=fig1,
units='inches')
pylab.text(xmin,
ymin,
"time = %7.3g s" % (time),
verticalalignment="bottom",
transform=trans,
clip_on=False,
fontsize=10)
if (not annotation == ""):
trans = matplotlib.transforms.offset_copy(ax.transData,
x=0,
y=-0.65,
fig=fig1,
units='inches')
pylab.text(xmin,
ymin,
"%s" % (annotation),
verticalalignment="bottom",
transform=trans,
clip_on=False,
fontsize=10)
#----------------------------------------------------------------------
# plot component #2
#----------------------------------------------------------------------
if (not component2 == ""):
ax = pylab.subplot(1, 2, 2)
divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)
im = pylab.imshow(data2[iy0:iy, ix0:ix],
origin='lower',
extent=extent,
vmin=minval2,
vmax=maxval2)
pylab.title(component2)
pylab.xlabel("x")
pylab.ylabel("y")
# axis labels in scientific notation with LaTeX
ax.xaxis.set_major_formatter(
pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(
pylab.ScalarFormatter(useMathText=True))
if (sparseX):
ax.xaxis.set_major_locator(pylab.MaxNLocator(3))
# make space for a colorbar -- getting it the same size as
# the vertical extent of the plot is surprisingly tricky.
# See
# http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#colorbar-whose-height-or-width-in-sync-with-the-master-axes
ax_cb = divider.new_horizontal(size="10%", pad=0.1)
fig1 = ax.get_figure()
fig1.add_axes(ax_cb)
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cb = pylab.colorbar(im, format=formatter, cax=ax_cb)
# make the font size for the colorbar axis labels small. Note the
# offsetText is the 10^N that appears at the top of the y-axis.
cl = pylab.getp(cb.ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cb.ax.yaxis.offsetText.set_fontsize("small")
#ax_cb.yaxis.tick_right()
else:
#----------------------------------------------------------------------
# 3-d plot
#----------------------------------------------------------------------
# starting points for the figure positions
# assume that the width of the plotting area is 0.05 to 0.95,
# leaving 0.9 to be split amongst the 3 plots. So each has a
# width of 0.3
# for the height, we will assume that the colorbar at the
# bottom gets 0.15, and that we go until 0.95, leaving 0.8 of
# height for the plots.
pos1 = [0.05, 0.15, 0.3, 0.8]
pos2 = [0.35, 0.15, 0.3, 0.8]
pos3 = [0.65, 0.15, 0.3, 0.8]
fig = pylab.figure()
# read in the slices
# x-y
data_xy = numpy.zeros((nx, ny), dtype=numpy.float64)
indir = 3
(data_xy, err) = \
fsnapshot.fplotfile_get_data_3d(plotfile, component, indir,
origin, data_xy)
if (not err == 0):
sys.exit(2)
data_xy = numpy.transpose(data_xy)
if log:
if (numpy.min(data_xy) < 0):
data_xy = numpy.log10(numpy.abs(data_xy))
else:
data_xy = numpy.log10(data_xy)
# x-z
data_xz = numpy.zeros((nx, nz), dtype=numpy.float64)
(data_xz, err) = \
fsnapshot.fplotfile_get_data_3d(plotfile, component, 2,
origin, data_xz)
if (not err == 0):
sys.exit(2)
data_xz = numpy.transpose(data_xz)
if log:
if (numpy.min(data_xz) < 0):
data_xz = numpy.log10(numpy.abs(data_xz))
else:
data_xz = numpy.log10(data_xz)
# y-z
data_yz = numpy.zeros((ny, nz), dtype=numpy.float64)
(data_yz, err) = \
fsnapshot.fplotfile_get_data_3d(plotfile, component, 1,
origin, data_yz)
if (not err == 0):
sys.exit(2)
data_yz = numpy.transpose(data_yz)
if log:
if (numpy.min(data_yz) < 0):
data_yz = numpy.log10(numpy.abs(data_yz))
else:
data_yz = numpy.log10(data_yz)
if (not minval == None):
if (log):
minval = math.log10(minval)
else:
minval = numpy.min(data_xy)
minval = min(minval, numpy.min(data_xz))
minval = min(minval, numpy.min(data_yz))
if (not maxval == None):
if (log):
maxval = math.log10(maxval)
else:
maxval = numpy.max(data_xy)
maxval = max(maxval, numpy.max(data_xz))
maxval = max(maxval, numpy.max(data_yz))
# x-y
extent = [xmin, xmax, ymin, ymax]
ix0 = 0
ix = nx
iy0 = 0
iy = ny
if (not xmin_pass == None):
extent[0] = xmin_pass
ix0 = int((xmin_pass - xmin) / dx)
if (not ymin_pass == None):
extent[2] = ymin_pass
iy0 = int((ymin_pass - ymin) / dy)
if (not xmax_pass == None):
extent[1] = xmax_pass
ix = int((xmax_pass - xmin) / dx)
if (not ymax_pass == None):
extent[3] = ymax_pass
iy = int((ymax_pass - ymin) / dy)
ax = pylab.subplot(1, 3, 1)
pylab.subplots_adjust(wspace=0.4)
#fig.add_axes(pos1)
im = pylab.imshow(data_xy[iy0:iy, ix0:ix],
origin='lower',
extent=extent,
vmin=minval,
vmax=maxval,
axes=pos1)
pylab.xlabel("x")
pylab.ylabel("y")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.xaxis.offsetText.set_fontsize("small")
ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cl = pylab.getp(ax, 'xmajorticklabels')
pylab.setp(cl, fontsize=10)
# do a fixed offset in pixels from the (xmin,ymin) data point
fig1 = ax.get_figure()
trans = matplotlib.transforms.offset_copy(ax.transData,
x=0,
y=-0.5,
fig=fig1,
units='inches')
# pylab.text(xmin_pass, ymin_pass, "time = %7.3g s" % (time),
# verticalalignment="bottom", transform = trans,
# clip_on=False, fontsize=10)
# x-z
extent = [xmin, xmax, zmin, zmax]
ix0 = 0
ix = nx
iz0 = 0
iz = nz
if (not xmin_pass == None):
extent[0] = xmin_pass
ix0 = int((xmin_pass - xmin) / dx)
if (not zmin_pass == None):
extent[2] = zmin_pass
iz0 = int((zmin_pass - zmin) / dz)
if (not xmax_pass == None):
extent[1] = xmax_pass
ix = int((xmax_pass - xmin) / dx)
if (not zmax_pass == None):
extent[3] = zmax_pass
iz = int((zmax_pass - zmin) / dz)
ax = pylab.subplot(1, 3, 2)
#fig.add_axes(pos2)
im = pylab.imshow(data_xz[iz0:iz, ix0:ix],
origin='lower',
extent=extent,
vmin=minval,
vmax=maxval,
axes=pos2)
pylab.xlabel("x")
pylab.ylabel("z")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.xaxis.offsetText.set_fontsize("small")
ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cl = pylab.getp(ax, 'xmajorticklabels')
pylab.setp(cl, fontsize=10)
# y-z
extent = [ymin, ymax, zmin, zmax]
iy0 = 0
iy = ny
iz0 = 0
iz = nz
if (not ymin_pass == None):
extent[0] = ymin_pass
iy0 = int((ymin_pass - ymin) / dy)
if (not zmin_pass == None):
extent[2] = zmin_pass
iz0 = int((zmin_pass - zmin) / dz)
if (not ymax_pass == None):
extent[1] = ymax_pass
iy = int((ymax_pass - ymin) / dy)
if (not zmax_pass == None):
extent[3] = zmax_pass
iz = int((zmax_pass - zmin) / dz)
ax = pylab.subplot(1, 3, 3)
#fig.add_axes(pos3)
im = pylab.imshow(data_yz[iz0:iz, iy0:iy],
origin='lower',
extent=extent,
vmin=minval,
vmax=maxval,
axes=pos3)
pylab.xlabel("y")
pylab.ylabel("z")
# axis labels in scientific notation with LaTeX
ax = pylab.gca()
ax.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
ax.xaxis.offsetText.set_fontsize("small")
ax.yaxis.offsetText.set_fontsize("small")
cl = pylab.getp(ax, 'ymajorticklabels')
pylab.setp(cl, fontsize=10)
cl = pylab.getp(ax, 'xmajorticklabels')
pylab.setp(cl, fontsize=10)
# colorbar
pylab.subplots_adjust(bottom=0.1, left=0.05, right=0.95)
formatter = matplotlib.ticker.ScalarFormatter(useMathText=True)
cax = pylab.axes([0.05, 0.06, 0.9, 0.04])
pylab.colorbar(orientation="horizontal", cax=cax, format=formatter)
pylab.title(component)
#--------------------------------------------------------------------------
# save the figure
#--------------------------------------------------------------------------
# try: pylab.tight_layout() # requires matplotlib >= 1.1
# except:
# pass
if (not eps):
pylab.savefig(outFile, bbox_inches='tight', dpi=dpi, pad_inches=0.33)
else:
pylab.savefig(outFile) #, bbox_inches='tight', pad_inches=0.33)
def plot_average_size_tuning_curve(self,independent=True):
if independent:
fig = pylab.figure()
average_curves={}
sem_curves={}
curves={}
for k in self.data_dict.keys():
# find maximum for the curves
m = []
for curve_label in self.data_dict[k]["ST"].keys():
xindex, yindex = k
curve = self.data_dict[k]["ST"][curve_label]["data"]
x_values = sorted(curve.keys())
m.append(numpy.max([curve[key].view()[0][xindex, yindex] for key in x_values]))
m = numpy.max(m)
for curve_label in self.data_dict[k]["ST"].keys():
xindex, yindex = k
curve = self.data_dict[k]["ST"][curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[key].view()[0][xindex, yindex] for key in x_values]
# normalize curve
y_values = y_values / m
if curves.has_key(curve_label):
curves[curve_label].append(numpy.array(y_values))
else:
curves[curve_label]=[numpy.array(y_values)]
for curve_label in curves:
average_curves[curve_label]=numpy.mean(numpy.array(curves[curve_label]),axis=0)
sem_curves[curve_label]=scipy.stats.sem(numpy.array(curves[curve_label]),axis=0)
colors=['red','blue']
i=0
for curve_label in average_curves:
pylab.plot(x_values, average_curves[curve_label]*100, lw=3, color=colors[i])
pylab.errorbar(x_values, average_curves[curve_label]*100, lw=1, ecolor=colors[i], yerr=sem_curves[curve_label]*100, fmt=None)
pylab.xticks([0,numpy.max(x_values)/2,numpy.max(x_values)])
pylab.yticks([0,50,100])
pylab.gca().set_yticklabels(['0%','50%','100%'])
pylab.xlim(-0.1,numpy.max(x_values)+0.1)
pylab.ylim(0,100)
i=i+1
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
if independent:
release_fig("AverageSTC")
