def create_hpxmap_hist_figure():
#fig = plt.figure(figsize=(10.5,4))
#gridspec=plt.GridSpec(1, 3)
fig = plt.figure(figsize=(12., 4.))
gridspec = plt.GridSpec(1, 3, wspace=1.0)
#gridspec.update(left=0.07,right=0.91,bottom=0.15,top=0.95,wspace=0.08)
return fig, gridspec
def Plot(x, y, legx='', legy=''):
"""
fig = pl.figure()
grid = pl.GridSpec(2, 2)
print(grid)
ax = fig.add_subplot(grid[0, :])
pl.plot(tab[whatx],tab[whaty],'k.')
ax = fig.add_subplot(grid[1, 0])
ax.hist(tab[whatx],histtype='step')
ax = fig.add_subplot(grid[1, 1])
ax.hist(tab[whaty],histtype='step')
"""
fig = pl.figure(figsize=(10, 10))
grid = pl.GridSpec(4, 4, hspace=0.8, wspace=0.8)
main_ax = fig.add_subplot(grid[:-1, 1:])
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)
x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)
# scatter points on the main axes
#main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)
main_ax.plot(x, y, 'ok', markersize=1.)
# histogram on the attached axes
x_hist.hist(x, 40, histtype='step', orientation='vertical', color='red')
#x_hist.invert_yaxis()
y_hist.hist(y, 40, histtype='step', orientation='horizontal', color='blue')
y_hist.invert_xaxis()
main_ax.set_xlabel(legx)
main_ax.set_ylabel(legy)
def plot_hpxmap(hpxmap,
raRange=[-180, 180],
decRange=[-90, 90],
lonRef=0.0,
percRange=[0.1, 99.9],
figsize=(6.5, 4),
cbar_kwargs=dict(),
hpxmap_kwargs=dict()):
if isinstance(hpxmap, basestring):
hpxmap = healpy.read_map(f)
fig = plt.figure(10, figsize=figsize)
fig.clf()
gridspec = plt.GridSpec(1, 2)
bmap = FgcmBasemap(lonRef=lonRef,
raMin=raRange[0],
raMax=raRange[1],
decMin=decRange[0],
decMax=decRange[1])
bmap.create_axes(rect=gridspec[0:2])
im = bmap.draw_hpxmap(hpxmap, percRange=percRange, **hpxmap_kwargs)
bmap.draw_inset_colorbar(**cbar_kwargs)
ax = plt.gca()
ax.axis['right'].major_ticklabels.set_visible(False)
ax.axis['top'].major_ticklabels.set_visible(False)
fig.subplots_adjust(bottom=0.15, top=0.95)
return fig, ax
def anim_setup():
fig = py.figure()
# Define a 2 x 2 grid
gs = py.GridSpec(2, 2) # 2 rows, 2 columns
# Define 3 subplots. First one spanning both rows of column 1, the rest two taking the ramining 2 subplots
ax_xy = fig.add_subplot(gs[:, 0],
aspect=1) # xy plane (column left, both rows)
ax_yz = fig.add_subplot(gs[0, 1],
aspect=1) # yz plane (column right, top row)
ax_xz = fig.add_subplot(gs[1, 1],
aspect=1) # xz plane (column right, bottoom row)
# Set figure dpi and size
fig.set_dpi(100)
fig.set_size_inches(14, 9)
# Lower and upper limit of the axes of the three subplots
ax_xy.set_xlim(x_lim[0], x_lim[1])
ax_xy.set_ylim(y_lim[0], y_lim[1])
ax_yz.set_xlim(y_lim[0], y_lim[1])
ax_yz.set_ylim(z_lim[0], z_lim[1])
ax_xz.set_xlim(x_lim[0], x_lim[1])
ax_xz.set_ylim(z_lim[0], z_lim[1])
# Initialize bead patches
beads_xy = []
beads_yz = []
beads_xz = []
for p in range(Np):
cl_p = particle_color(p) # Defined in geometry_draw_X.py module
beads_xy.append(
plt.patches.Circle((r_um[0, p, 0], r_um[1, p, 0]),
ro[p] * 1e6,
fc=cl_p,
ec='k',
zorder=10))
beads_yz.append(
plt.patches.Circle((r_um[1, p, 0], r_um[2, p, 0]),
ro[p] * 1e6,
fc=cl_p,
ec='k',
zorder=10))
beads_xz.append(
plt.patches.Circle((r_um[0, p, 0], r_um[2, p, 0]),
ro[p] * 1e6,
fc=cl_p,
ec='k',
zorder=10))
time_template = 'Time = %.1f s'
time_string = ax_xy.text(0.05, 0.90, '', transform=ax_xy.transAxes)
text_string1 = ax_xy.text(0.05, 0.85, '', transform=ax_xy.transAxes)
text_string2 = ax_xy.text(0.05, 0.80, '', transform=ax_xy.transAxes)
return fig, ax_xy, ax_yz, ax_xz, beads_xy, beads_yz, beads_xz, time_template, time_string, text_string1, text_string2
def Plot(med_log,
bands,
season,
whata,
whatb,
simu_name,
thetype,
saveplot=False):
if saveplot:
dirout = 'Plots_' + simu_name + '_' + thetype + '/Season_' + str(
season)
if not os.path.isdir(dirout):
os.makedirs(dirout)
for band in bands:
idxb = med_log['band'] == band
sel = med_log[idxb]
"""
figa, axa = plt.subplots(ncols=1, nrows=1)
figa.suptitle(band+' band')
axa.plot(sel[whata],sel[whatb],'k.')
axa.set_xlabel(whata)
axa.set_ylabel(whatb)
"""
fig = plt.figure(figsize=(10, 10))
fig.suptitle('Season ' + str(season) + ' - ' + band + ' band')
grid = plt.GridSpec(4, 4, hspace=0.8, wspace=0.8)
main_ax = fig.add_subplot(grid[:-1, 1:])
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)
x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)
# scatter points on the main axes
#main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)
main_ax.plot(sel[whata], sel[whatb], 'ok', markersize=1.)
# histogram on the attached axes
x_hist.hist(sel[whata],
40,
histtype='step',
orientation='vertical',
color='red')
#x_hist.invert_yaxis()
y_hist.hist(sel[whatb],
40,
histtype='step',
orientation='horizontal',
color='blue')
y_hist.invert_xaxis()
main_ax.set_xlabel(whata)
main_ax.set_ylabel(whatb)
if saveplot:
plt.gcf().savefig(dirout + '/' + whata + '_' + whatb + '_' + band +
'.png',
bbox_inches='tight')
def anim_setup():
fig = py.figure()
# Define a 2 x 2 grid
gs = py.GridSpec(2, 2) # 2 rows, 2 columns
# Define 3 subplots. First one spanning both rows of column 1, the rest two taking the ramining 2 subplots
ax1 = fig.add_subplot(gs[:, 0],
aspect=1) # xy plane (column left, both rows)
ax2 = fig.add_subplot(gs[0, 1],
aspect=1) # yz plane (column right, top row)
ax3 = fig.add_subplot(gs[1, 1],
aspect=1) # xz plane (column right, bottoom row)
# Set figure dpi and size
fig.set_dpi(100)
fig.set_size_inches(14, 6)
# Lower and upper limit of the axes of the three subplots
ax1.set_xlim(-1e6 * xrange_limit, 1e6 * xrange_limit)
ax1.set_ylim(-1e6 * xrange_limit, 1e6 * xrange_limit)
ax2.set_xlim(-1e6 * xrange_limit, 1e6 * xrange_limit)
ax2.set_ylim(1e6 * zlow_limit, 1e6 * zhigh_limit)
ax3.set_xlim(-1e6 * xrange_limit, 1e6 * xrange_limit)
ax3.set_ylim(1e6 * zlow_limit, 1e6 * zhigh_limit)
# Initialize bead patches
beads_xy = []
beads_yz = []
beads_xz = []
for p in range(Np):
beads_xy.append(
plt.patches.Circle((r_um[0, p, 0], r_um[1, p, 0]),
ro[p] * 1e6,
fc='#C21808',
ec='k',
zorder=10))
beads_yz.append(
plt.patches.Circle((r_um[1, p, 0], r_um[2, p, 0]),
ro[p] * 1e6,
fc='#C21808',
ec='k',
zorder=10))
beads_xz.append(
plt.patches.Circle((r_um[0, p, 0], r_um[2, p, 0]),
ro[p] * 1e6,
fc='#C21808',
ec='k',
zorder=10))
time_template = 'Time = %.1f s'
time_string = ax1.text(0.05, 0.9, '', transform=ax1.transAxes)
return fig, ax1, ax2, ax3, beads_xy, beads_yz, beads_xz, time_template, time_string
def plot_neuron(tp):
fig2 = py.figure(figsize=(9, 6), dpi=200)
grid = py.GridSpec(8, 8, hspace=0.1, wspace=0.1)
ax = fig2.add_subplot(grid[:-1, 1:7], projection='3d')
ax.set_title('True CSD')
# ax.scatter(posxyz[0], posxyz[1], posxyz[2], alpha = 0.1, s = 20,
# c = vs[:, tp], cmap='PRGn', vmin = -60, vmax = 60, antialiased=True)
# ax.scatter(posswc[2, ::skip], posswc[3, ::skip], posswc[4, ::skip], alpha = 0.5, s = 8,
# c = vs[:1216, tp], cmap='PRGn', vmin = -60, vmax = 60, antialiased=True)
ax.scatter(posswc[3, ::skip],
posswc[2, ::skip],
posswc[4, ::skip],
alpha=0.1,
s=12,
c='grey',
linewidths=0,
antialiased=True)
ax.scatter(wsp_ele[1],
wsp_ele[0],
wsp_ele[2],
alpha=1,
s=6,
c=pots[tp, :],
cmap='PRGn',
vmin=-0.2,
vmax=0.2,
linewidths=0.5,
edgecolors='black',
antialiased=True)
ax.scatter(posxyz[1],
posxyz[0],
posxyz[2],
alpha=0.5,
s=15,
c=-ks[:, tp] - nas[:, tp] - pas[n, tp],
cmap=cm.bwr,
vmin=-0.3,
vmax=0.3,
antialiased=True)
ax3 = fig2.add_subplot(grid[-1, 1:-1])
ax3.plot(xtime, pots[:, 0], label='ele bot')
ax3.plot(xtime, pots[:, 15], label='ele mid')
ax3.plot(xtime, pots[:, 31], label='ele top')
py.legend()
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.spines['bottom'].set_visible(False)
ax3.set_xlim(100, 150)
ax3.axvline(xtime[tp], linestyle='--')
ax.view_init(elev=90, azim=0)
return fig2
def plot_facade_cuts(self):
facade_sig = self.facade_edge_scores.sum(0)
facade_cuts = find_facade_cuts(
facade_sig, dilation_amount=self.facade_merge_amount)
mu = np.mean(facade_sig)
sigma = np.std(facade_sig)
w = self.rectified.shape[1]
pad = 10
gs1 = pl.GridSpec(5, 5)
gs1.update(wspace=0.5, hspace=0.0) # set the spacing between axes.
pl.subplot(gs1[:3, :])
pl.imshow(self.rectified)
pl.vlines(facade_cuts, *pl.ylim(), lw=2, color='black')
pl.axis('off')
pl.xlim(-pad, w + pad)
pl.subplot(gs1[3:, :], sharex=pl.gca())
pl.fill_between(np.arange(w), 0, facade_sig, lw=0, color='red')
pl.fill_between(np.arange(w),
0,
np.clip(facade_sig, 0, mu + sigma),
color='blue')
pl.plot(np.arange(w), facade_sig, color='blue')
pl.vlines(facade_cuts,
facade_sig[facade_cuts],
pl.xlim()[1],
lw=2,
color='black')
pl.scatter(facade_cuts, facade_sig[facade_cuts])
pl.axis('off')
pl.hlines(mu, 0, w, linestyle='dashed', color='black')
pl.text(0, mu, '$\mu$ ', ha='right')
pl.hlines(
mu + sigma,
0,
w,
linestyle='dashed',
color='gray',
)
pl.text(0, mu + sigma, '$\mu+\sigma$ ', ha='right')
pl.xlim(-pad, w + pad)
def plot_score_test(inputs,test_parameter,test_scores,test_tuning_indexes,test_parameter_label,same_vmax=True,tuning_index_lims=(-0.03,0.6)):
"""
Plot gridness scores and grid-tuning indexes agains a test parameter (e.g., modulation depth, tuning strengths)
"""
ms=5
fig=pl.figure(figsize=(4,2.5))
pl.subplots_adjust(left=0.2,bottom=0.2,right=0.8)
num_cells=6
gs = pl.GridSpec(3,num_cells,hspace=0.35,wspace=0.1)
cell_idxs=np.linspace(0,n**2-1,num_cells).astype(int)
vmax=inputs.max()
for idx,cell_idx in enumerate(cell_idxs):
ax=fig.add_subplot(gs[0,idx],aspect='equal')
r_map=inputs[:,cell_idx].reshape(nx,nx).T
if same_vmax is True:
ax.pcolormesh(r_map,rasterized=True,vmin=0,vmax=vmax)
else:
ax.pcolormesh(r_map,rasterized=True,vmin=0)
pp.noframe(ax)
ax1 = fig.add_subplot(gs[1:3,0:num_cells])
ax1=pl.gca()
color = 'tab:red'
ax1.set_xlabel(test_parameter_label)
ax1.set_ylabel('Grid tuning index', color=color)
ax1.plot(test_parameter, test_tuning_indexes, color=color,marker='o',lw=0,alpha=.5,ms=ms,mec='none')
ax1.tick_params(axis='y', labelcolor=color)
ax1.set_ylim(tuning_index_lims)
ax1.spines['top'].set_color('none')
pl.plot(test_parameter[cell_idxs],np.ones_like(test_parameter[cell_idxs])*0,'vk')
ax2 = ax1.twinx()
color = 'tab:orange'
ax2.set_ylabel('Gridness score', color=color)
ax2.plot(test_parameter, test_scores, color=color,marker='o',lw=0,alpha=.5,ms=ms,mec='none')
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylim(-1,2)
ax2.spines['top'].set_color('none')
def plot_hpxmap_hist(hpxmap,
raRange=[-180, 180],
decRange=[-90, 90],
lonRef=0.0,
cbar_kwargs=dict(),
hpxmap_kwargs=dict(),
hist_kwargs=dict(),
fit_gaussian=False,
figsize=(10, 4)):
hist_defaults = dict(peak=True)
set_defaults(hist_kwargs, hist_defaults)
if isinstance(hpxmap, basestring):
hpxmap = healpy.read_map(f)
fig = plt.figure(10, figsize=figsize)
fig.clf()
gridspec = plt.GridSpec(1, 3)
bmap = FgcmBasemap(lonRef=lonRef,
raMin=raRange[0],
raMax=raRange[1],
decMin=decRange[0],
decMax=decRange[1])
bmap.create_axes(rect=gridspec[0:2])
im = bmap.draw_hpxmap(hpxmap, **hpxmap_kwargs)
bmap.draw_inset_colorbar(**cbar_kwargs)
ax1 = plt.gca()
ax1.axis['right'].major_ticklabels.set_visible(False)
ax1.axis['top'].major_ticklabels.set_visible(False)
ax2 = Subplot(fig, gridspec[2])
fig.add_subplot(ax2)
plt.sca(ax2)
ret = draw_hist(hpxmap, fit_gaussian=fit_gaussian, **hist_kwargs)
ax2.yaxis.set_major_locator(MaxNLocator(6, prune='both'))
ax2.xaxis.set_major_locator(MaxNLocator(5))
ax2.axis['left'].major_ticklabels.set_visible(False)
ax2.axis['right'].major_ticklabels.set_visible(True)
ax2.axis['right'].label.set_visible(True)
ax2.axis['right'].label.set_text(r'Normalized Area (a.u.)')
ax2.axis['bottom'].label.set_visible(True)
plt.subplots_adjust(bottom=0.15, top=0.95)
return fig, [ax1, ax2], ret
def draw():
#-----Simulation map plot-----------------------------------------------------------------
grid = pl.GridSpec(3, 2, wspace=0.4, hspace=0.3)
pl.subplot(grid[0:, 0])
pl.cla()
pl.pcolor(envir, cmap = pl.cm.YlOrRd, vmin = 0, vmax = 15)
pl.axis('image')
pl.hold(True)
# Plot the walls on the matrix
pl.scatter(np.add(wall_x, [0.5]), np.add(wall_y, [0.5]), marker = "s", c = 'gray')
# Plot the office on the matrix
pl.scatter(np.add(office_x, [0.5]), np.add(office_y, [0.5]), marker = "s", c = 'gray')
# Plot the gates on the matrix
pl.scatter(np.add(gate_x, [0.5]), np.add(gate_y, [0.5]), marker = "s", c = 'green')
# For each agent plot: location, intention, goal
for ag in agents:
# Plot the agent's location on the matrix
x, y = ag.get_pos()
pl.scatter(x + 0.5, y + 0.5, c = "dark" + ag.color)
if(len(ag.goal) > 0):
goals = ag.goal
# Plot the agent's goal on the matrix
pl.scatter(goals[1] + 0.5, goals[0] + 0.5, marker = "x", c = "green")
pl.title('t = ' + str(time))
#-----Stats text: Orders -----------------------------------------------------------------
ax2 = pl.subplot(grid[0, 1])
ax2.axis("off")
ax2.invert_yaxis()
to_plot_string1 = "\nTotal orders: "+ total_numb_orders + "\nDone orders: "+ str(len(orders_list[orders_list["status"] == 2].index))
ax2.text(0,0, to_plot_string1, verticalalignment="top")
#-----1° Stats plot: Articles-----------------------------------------------------------------
pl.subplot(grid[1, 1])
x = range(0, len(total_stats))
pl.plot(x, total_stats["Articles"], color = 'blue')
pl.title('Articles')
#-----2° Stats plot: Conflicts-----------------------------------------------------------------
pl.subplot(grid[2, 1])
x = range(0, len(total_stats))
pl.plot(x, total_stats["Conflicts"], color = 'red')
pl.title('Conflicts')
pl.hold(False)
def draw(self):
fig = _pylab.figure(1)
gs = _pylab.GridSpec(2, 1, height_ratios=(1, 4))
axMachine = _pylab.subplot(gs[0])
axMachine.axes.get_yaxis().set_visible(False)
self.drawMachine()
axOptics = _pylab.subplot(gs[1])
self.plotOptics()
def machineXlim(ax):
axMachine.set_autoscale_on(False)
axOptics.set_xlim(axMachine.get_xlim())
def click(a):
if a.button == 3:
print self.tfs.NameFromNearestS(a.xdata)
axMachine.callbacks.connect('xlim_changed', machineXlim)
fig.canvas.mpl_connect('button_press_event', click)
plt.show()
plt.imshow(np.resize(mean, (26, 26)), cmap=plt.cm.gray)
plt.title('mean')
plt.show()
diff = np.zeros((213, 676))
for i in range(len(data)):
diff[i] = data[i].astype('float64') - mean
# end for
plt.imshow(np.resize(diff[0], (26, 26)), cmap=plt.cm.gray)
plt.title('diff image #1')
plt.show()
layout = pylab.GridSpec(213 + 1, 4)
fig = plt.figure()
subplt = fig.add_subplot(layout[0, 0])
subplt.set_title('#')
subplt = fig.add_subplot(layout[0, 1])
subplt.set_title('o')
subplt = fig.add_subplot(layout[0, 2])
subplt.set_title('m')
subplt = fig.add_subplot(layout[0, 3])
subplt.set_title('d')
for i in range(len(data))[::50]:
subplt = fig.add_subplot(layout[i + 1, 0])
subplt.set_title('#{}'.format(i))
subplt = fig.add_subplot(layout[i + 1, 1])
def plot_tracking(frame, pos, target_sz, im, ground_truth):
global tracking_figure, tracking_figure_title, tracking_figure_axes, tracking_rectangle, gt_point,\
z_figure_axes, response_figure_axes, z, response
timeout = 1e-6
# timeout = 0.05 # uncomment to run slower
if frame == 0:
# pylab.ion() # interactive mode on
tracking_figure = pylab.figure()
gs = pylab.GridSpec(1, 3, width_ratios=[3, 1, 1])
tracking_figure_axes = tracking_figure.add_subplot(gs[0])
tracking_figure_axes.set_title("Tracked object (and ground truth)")
z_figure_axes = tracking_figure.add_subplot(gs[1])
z_figure_axes.set_title("Template")
response_figure_axes = tracking_figure.add_subplot(gs[2])
response_figure_axes.set_title("Response")
tracking_rectangle = pylab.Rectangle((0, 0), 0, 0)
tracking_rectangle.set_color((1, 0, 0, 0.5))
tracking_figure_axes.add_patch(tracking_rectangle)
gt_point = pylab.Circle((0, 0), radius=5)
gt_point.set_color((0, 0, 1, 0.5))
tracking_figure_axes.add_patch(gt_point)
tracking_figure_title = tracking_figure.suptitle("")
pylab.show(block=False)
elif tracking_figure is None:
return # we simply go faster by skipping the drawing
elif not pylab.fignum_exists(tracking_figure.number):
# print("Drawing window closed, end of game. "
# "Have a nice day !")
# sys.exit()
print("From now on drawing will be omitted, "
"so that computation goes faster")
tracking_figure = None
return
tracking_figure_axes.imshow(im, cmap=pylab.cm.gray)
rect_y, rect_x = tuple(pos - target_sz / 2.0)
rect_height, rect_width = target_sz
tracking_rectangle.set_xy((rect_x, rect_y))
tracking_rectangle.set_width(rect_width)
tracking_rectangle.set_height(rect_height)
if len(ground_truth) > 0:
gt = ground_truth[frame]
gt_y, gt_x = gt
gt_point.center = (gt_x, gt_y)
if z is not None:
z_figure_axes.imshow(z, cmap=pylab.cm.hot)
if response is not None:
response_figure_axes.imshow(response, cmap=pylab.cm.hot)
tracking_figure_title.set_text("Frame %i (out of %i)" %
(frame + 1, len(ground_truth)))
if debug and False and (frame % 1) == 0:
print("Tracked pos ==", pos)
# tracking_figure.canvas.draw() # update
pylab.draw()
pylab.waitforbuttonpress(timeout=timeout)
return
def log_tracked(image, tracked_roi, cached, template_f, response_f):
timeout = 1e-6
tracking_figure_axes = results.tracking_figure_axes
tracking_figure = results.tracking_figure
tracking_figure_title = results.tracking_figure_title
response_figure_axes = results.response_figure_axes
tracking_rectangle = results.tracking_rectangle
gt_rectangle = results.gt_rectangle
template_axes = results.template_axes
if not results.initialized:
# pylab.ion() # interactive mode on
tracking_figure = results.tracking_figure = pylab.figure()
gs = pylab.GridSpec(1, 3, width_ratios=[3, 1, 1])
tracking_figure_axes = results.tracking_figure_axes = tracking_figure.add_subplot(gs[0])
tracking_figure_axes.set_title("Tracked object (and ground truth)")
template_axes = results.template_axes = tracking_figure.add_subplot(gs[1])
template_axes.set_title("Template")
response_figure_axes = results.response_figure_axes = tracking_figure.add_subplot(gs[2])
response_figure_axes.set_title("Response")
tracking_rectangle = results.tracking_rectangle = pylab.Rectangle((0, 0), 0, 0)
tracking_rectangle.set_color((1, 1, 0, 0.5))
tracking_figure_axes.add_patch(tracking_rectangle)
gt_rectangle = results.gt_rectangle = pylab.Rectangle((0, 0), 0, 0)
gt_rectangle.set_color((0, 0, 1, 0.5))
tracking_figure_axes.add_patch(gt_rectangle)
tracking_figure_title = results.tracking_figure_title = tracking_figure.suptitle("")
pylab.show(block=False)
elif tracking_figure is None:
return # we simply go faster by skipping the drawing
elif not pylab.fignum_exists(tracking_figure.number):
print("From now on drawing will be omitted, "
"so that computation goes faster")
results.omitted_at_frame_number = loader.frame_number()
results.tracking_figure = None
return
tracking_figure_axes.imshow(image)
tracking_rectangle.set_bounds(tracked_roi[0] - tracked_roi[2] / 2, tracked_roi[1] - tracked_roi[3] / 2, tracked_roi[2], tracked_roi[3])
gt = loader.get_gt_bounding_box()
gt_rectangle.set_bounds(gt[0], gt[1], gt[2], gt[3])
if template_f is not None:
template_axes.imshow(template_f, cmap=pylab.cm.hot)
if response_f is not None:
response_figure_axes.imshow(response_f, cmap=pylab.cm.hot)
tracking_rectangle.set_color((0 if not cached else 1, 0.5 if not cached else 0, 0, 0.7 if not cached else 0.2))
tracking_figure_title.set_text("Frame {}".format(loader.frame_number()))
pylab.draw()
if results.initialized:
pylab.savefig(loader.get_log_dir() + 'image%05i.jpg' % loader.frame_number(), bbox_inches='tight')
pylab.waitforbuttonpress(timeout=timeout)
results.initialized = True
return
def run_single_tilt_angle(ang, subtomogram, offset, vol_size,
particle_position, particle_rotation,
particle_filename, particle_number, binning, img,
create_graphics, fsc_path, dimz, peak_border):
"""
To run a single tilt angle to allow for parallel computing
@param ang: the tilt angle
@type ang: int
@param subtomogram: the filename of the subtomogram
@type subtomogram: str
@param offset: the offset used (x,y,z)
@type offset: list(int, int, int)
@param vol_size: the size of the volume to be reconstructed (in pixels)
@type vol_size: int
@param particle_position: the position of the particle in vector format,
as given by particle.pickPosition().toVector()
@type particle_position: tuple
@param particle_rotation: the rotation of the particle (Z1/phi, X/the, Z2/psi)
@type particle_rotation: tuple
@param particle_filename: the filename of the particle, as given by particle.getfilename()
@type particle_filename: str
@param particle_number: the number of the particle, to allow for unique mapping
@type particle_number: int
@param binning: the binning factor used
@type binning: int
@param img: the filename of the projection to be used
@type img: str
@param create_graphics: to flag if images should be created for human inspection of the work done
@type create_graphics: bool
@return: the newly found positions of the particle, as a list in the LOCAL_ALIGNMENT_RESULTS format
@returntype: list
"""
print(ang, offset, binning, particle_position)
from pytom.tompy.transform import rotate3d
import numpy as np
from math import cos, sin, pi
from pytom.tompy.transform import cut_from_projection
from pytom.tompy.io import read
subtomogram = read(subtomogram)
img = read(img)
# Get the size of the original projection
dim_x = img.shape[0]
dim_z = dim_x if dimz is None else dimz
print(dim_x, dim_z)
x, y, z = particle_position
x = (x + offset[0]) * binning
y = (y + offset[1]) * binning
z = (z + offset[2]) * binning
# Get template
# First rotate towards orientation of the particle, then to the tilt angle
rotated1 = rotate3d(subtomogram,
phi=particle_rotation[0],
the=particle_rotation[1],
psi=particle_rotation[2])
rotated2 = rotate3d(rotated1, the=ang) # 'the' is the rotational axis
template = rotated2.sum(axis=2)
# Get coordinates of the paricle adjusted for the tilt angle
yy = y # assume the rotation axis is around y
xx = (cos(ang * pi / 180) * (x - dim_x / 2) - sin(ang * pi / 180) *
(z - dim_z / 2)) + dim_x / 2
# Cut the small patch out
patch = cut_from_projection(img.sum(axis=2), [xx, yy],
[vol_size, vol_size])
patch = patch - patch.mean()
# Filter using FSC
fsc_mask = None
import os
if os.path.isfile(fsc_path):
f = open(fsc_path, "r")
fsc = map(lambda a: float(a), f.readlines())
f.close()
fsc_mask = create_fsc_mask(fsc, vol_size)
elif fsc_path != "":
print("Not an existing FSC file: " + fsc_path)
# Cross correlate the template and patch, this should give the pixel shift it is after
ccf = normalised_cross_correlation(template, patch, fsc_mask)
points2d = find_sub_pixel_max_value_2d(ccf, ignore_border=peak_border)
x_diff = points2d[0] - vol_size / 2
y_diff = points2d[1] - vol_size / 2
if create_graphics:
# Create an image to display and/or test the inner workings of the algorithm
m_style = dict(color='tab:blue',
linestyle=':',
marker='o',
markersize=5,
markerfacecoloralt='tab:red')
m_style_alt = dict(color='tab:red',
linestyle=':',
marker='o',
markersize=5,
markerfacecoloralt='tab:blue')
points = find_sub_pixel_max_value(ccf)
nx, ny, nz = particle_position
nx += x_diff
ny += y_diff
npatch = cut_from_projection(
img.sum(axis=2), [xx + x_diff, yy + y_diff], [vol_size, vol_size]
) # img.sum(axis=2)[int(xx+x_diff-v):int(xx+x_diff+v), int(yy+y_diff-v):int(yy+y_diff+v)] #
npatch = npatch - np.mean(npatch)
nccf = normalised_cross_correlation(template, npatch.squeeze())
npoints = find_sub_pixel_max_value(nccf)
npoints2d = find_sub_pixel_max_value_2d(nccf,
ignore_border=peak_border)
import pylab as pp
grid = pp.GridSpec(3,
3,
wspace=0,
hspace=0.35,
left=0.05,
right=0.95,
top=0.90,
bottom=0.05)
ax_0_0 = pp.subplot(grid[0, 0])
ax_0_1 = pp.subplot(grid[0, 1])
ax_0_2 = pp.subplot(grid[0, 2])
ax_1_0 = pp.subplot(grid[1, 0])
ax_1_1 = pp.subplot(grid[1, 1])
ax_1_2 = pp.subplot(grid[1, 2])
ax_2_0 = pp.subplot(grid[2, 0])
ax_2_1 = pp.subplot(grid[2, 1])
ax_2_2 = pp.subplot(grid[2, 2])
ax_0_0.axis('off')
ax_0_1.axis('off')
ax_0_2.axis('off')
ax_1_0.axis('off')
ax_1_1.axis('off')
ax_1_2.axis('off')
ax_2_0.axis('off')
ax_2_1.axis('off')
ax_2_2.axis('off')
axis_title(ax_0_0, "Cutout")
ax_0_0.imshow(patch)
axis_title(ax_0_1, "Template")
ax_0_1.imshow(template)
axis_title(ax_0_2, "Shifted Cutout\n(based on cross correlation)")
ax_0_2.imshow(npatch.squeeze())
axis_title(ax_1_0, u"Cross correlation\ncutout × template")
ax_1_0.imshow(ccf)
ax_1_0.plot([p[1] for p in points], [p[0] for p in points],
fillstyle='none',
**m_style)
ax_1_0.plot([points2d[1]], [points2d[0]],
fillstyle='none',
**m_style_alt)
ax_1_0.plot([vol_size / 2], [vol_size / 2], ",k")
ax_1_1.text(
0.5,
0.8,
"Red: 2D spline interpolation\nx: {:f}\ny: {:f}\nBlue: 1D spline interpolation\nx: {:f}\ny: {:f}"
"\nBlack: center".format(x_diff, y_diff,
points[0][0] - vol_size / 2,
points[0][1] - vol_size / 2),
fontsize=8,
horizontalalignment='center',
verticalalignment='center',
transform=ax_1_1.transAxes)
axis_title(ax_1_2, u"Cross correlation\nshifted cutout × template")
ax_1_2.imshow(nccf)
ax_1_2.plot([p[0] for p in npoints], [p[1] for p in npoints],
fillstyle='none',
**m_style)
ax_1_2.plot([npoints2d[0]], [npoints2d[1]],
fillstyle='none',
**m_style_alt)
ax_1_2.plot([vol_size / 2], [vol_size / 2], ",k")
axis_title(ax_2_0, u"Zoom into red peak\nin CC cutout × template")
d = 10
peak = ccf[int(points2d[0]) - d:int(points2d[0]) + d,
int(points2d[1]) - d:int(points2d[1] + d)]
ax_2_0.imshow(peak)
axis_title(
ax_2_1,
u"Zoom into red peak\nin CC cutout × template\ninterpolated")
ax_2_1.imshow(points2d[2])
axis_title(ax_2_2, u"Cutout\nGaussian filter σ3")
import scipy
ax_2_2.imshow(scipy.ndimage.gaussian_filter(patch, 3))
pp.savefig("polish_particle_{:04d}_tiltimage_{:05.2f}.png".format(
particle_number, ang))
return particle_number, x_diff, y_diff, ang, 0, 0, particle_filename
def make_plots_seg(data_dict, max_events, normed_img, pred_dict):
"""
Copy of make_plots adapted for pid plots
"""
target_plane_codes = {9: 1, 18: 2, 27: 3, 36: 6, 45: 4, 50: 5}
pkeys = []
for k in data_dict.keys():
if len(data_dict[k]) > 0:
pkeys.append(k)
print('Data dictionary present keys: {}'.format(pkeys))
types = ['energy', 'time']
views = ['x', 'u', 'v'] # TODO? build dynamically?
# only working with two-deep imgs these days
# plotting_two_tensors = True
def get_maybe_missing(data_dict, key, counter):
try:
return data_dict[key][counter]
except KeyError:
pass
return -1
evt_plotted = 0
with PdfPages("evt_all.pdf") as pdf:
for counter in range(len(data_dict[EVENTIDS])):
evtid = data_dict[EVENTIDS][counter]
segment = get_maybe_missing(data_dict, SEGMENTS, counter)
planecode = get_maybe_missing(data_dict, PLANECODES, counter)
n_hadmultmeas = get_maybe_missing(data_dict, N_HADMULTMEAS, counter)
(run, subrun, gate, phys_evt) = decode_eventid(evtid)
if evt_plotted > max_events:
break
status_string = 'Plotting entry %d: %d: ' % (counter, evtid)
title_string = '{}/{}/{}/{}'
title_elems = [run, subrun, gate, phys_evt]
if segment != -1 and planecode != -1:
title_string = title_string + ', segment {}, planecode {}'
title_elems.extend([segment, planecode])
if planecode in tuple(target_plane_codes.keys()):
title_string = title_string + ', targ {}'
title_elems.append(target_plane_codes[planecode[0]])
if n_hadmultmeas != -1:
title_string = title_string + ', n_chghad {}'
title_elems.append(n_hadmultmeas)
if pred_dict is not None:
try:
prediction = pred_dict[str(evtid)]
title_string = title_string + ', pred={}'
title_elems.append(prediction)
except KeyError:
pass
print(status_string + title_string.format(*title_elems))
# run, subrun, gate, phys_evt = decode_eventid(evtid)
fig_wid = 9
fig_height = 9
grid_height = 3
fig = pylab.figure(figsize=(fig_wid, fig_height))
fig.suptitle(title_string.format(*title_elems))
gs = pylab.GridSpec(grid_height, 3)
for i, t in enumerate(types):
datatyp = 'energies+times'
# set the bounds on the color scale
if normed_img:
minv = 0 if t == 'energy' else -1
maxv = 1
else:
maxes = []
mins = []
for v in views:
maxes.append(
np.abs(np.max(data_dict[datatyp][v][counter, i, :, :]))
)
mins.append(
np.abs(np.max(data_dict[datatyp][v][counter, i, :, :]))
)
minv = np.max(mins)
maxv = np.max(maxes)
maxex = maxv if maxv > minv else minv
minv = 0 if minv < 0.0001 else 0 if t == 'energy' else -maxv
maxv = maxex
for j, view in enumerate(views):
gs_pos = i * 3 + j
ax = pylab.subplot(gs[gs_pos])
ax.axis('on')
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
cmap = 'Reds' if t == 'energy' else 'bwr'
cbt = 'energy' if t == 'energy' else 'times'
datap = data_dict[datatyp][view][counter, i, :, :]
# make the plot
im = ax.imshow(
datap,
cmap=pylab.get_cmap(cmap),
interpolation='nearest',
vmin=minv, vmax=maxv
)
cbar = pylab.colorbar(im, fraction=0.04)
cbar.set_label(cbt, size=9)
cbar.ax.tick_params(labelsize=6)
pylab.title(t + ' - ' + view, fontsize=12)
pylab.xlabel('plane', fontsize=10)
pylab.ylabel('strip', fontsize=10)
# plot pid
for j, view in enumerate(views):
gs_pos = 6 + j
ax = pylab.subplot(gs[gs_pos])
ax.axis('on')
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
cmap = 'tab10'
cbt = 'pid'
datap = data_dict["pid"][view][counter, 0, :, :]
# make the plot
im = ax.imshow(
datap,
cmap=pylab.get_cmap(cmap),
interpolation='nearest',
vmin=0, vmax=7
)
cbar = pylab.colorbar(im, fraction=0.04, ticks=[0, 1, 2, 3, 4, 5, 6, 7])
cbar.ax.set_yticklabels(['nth',
'EM',
'mu',
'pi+',
'pi-',
'n',
'p',
'oth'])
cbar.set_label("pid", size=9)
cbar.ax.tick_params(labelsize=6)
pylab.title("pid" + ' - ' + view, fontsize=12)
pylab.xlabel('plane', fontsize=10)
pylab.ylabel('strip', fontsize=10)
pdf.savefig()
evt_plotted += 1
def f5_cadence_plot_new(lsstpg,
band,
lims=None,
median_log_summary=None,
median_log_summaryb=None,
dict_marker=None,
lims_sncosmo=None,
mag_range=(23., 26.5),
dt_range=(0.5, 15.),
target={},
targetb={},
alpha=1.,
simu_name='',
thetype='all',
seas=1,
save_it=False):
# SNR=dict(zip(['LSSTPG::' + b for b in 'grizy'],
# [20., 20., 20., 20., 10.]))):
dt = np.linspace(dt_range[0], dt_range[1], 50)
m5 = np.linspace(mag_range[0], mag_range[1], 50)
b = [band] * len(m5)
f5 = lsstpg.mag_to_flux(m5, b)
F5, DT = np.meshgrid(f5, dt)
M5, DT = np.meshgrid(m5, dt)
metric = np.sqrt(DT) * F5
#print('metric',metric)
sorted_keys = np.sort([k for k in lims.keys()])[::-1]
# draw limits
fig = plt.figure(figsize=(10, 10))
grid = plt.GridSpec(4, 4, hspace=0.8, wspace=0.8)
main_ax = fig.add_subplot(grid[:-1, 1:])
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)
x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)
main_ax.imshow(metric,
extent=(mag_range[0], mag_range[1], dt_range[0],
dt_range[1]),
aspect='auto',
alpha=0.25)
if median_log_summary is not None:
idx = median_log_summary['band'] == band
m = median_log_summary[idx]
main_ax.plot(m['m5'], m['cadence'], 'r+', alpha=alpha)
m5_exp = np.median(m['m5'])
idxb = (median_log_summary['band']
== band) & (median_log_summary['cadence'] <= 30)
m = median_log_summary[idxb]
#print(len(m),m['cadence'])
# histogram on the attached axes
x_hist.hist(m['m5'],
40,
histtype='step',
orientation='vertical',
color='red')
#x_hist.invert_yaxis()
y_hist.hist(m['cadence'],
40,
histtype='step',
orientation='horizontal',
color='blue')
y_hist.invert_xaxis()
"""
x_hist.set_xlabel('$m_{5\sigma}$', fontsize=18)
x_hist.set_ylabel('Number of Events', fontsize=18)
y_hist.set_xlabel('Observer frame cadence $^{-1}$ [days]', fontsize=16)
y_hist.set_ylabel('Number of Events', fontsize=18)
"""
#print 'h1',band,np.median(m['m5'])
if median_log_summaryb is not None:
"""
idx = median_log_summaryb['band'] == band
m = median_log_summaryb[idx]
plt.plot(m['m5'], m['cadence'], 'b+',alpha=alpha)
m5_exp=np.median(m['m5'])
"""
#print 'h2',band,np.median(m['m5'])
for key, val in median_log_summaryb.items():
idx = val['band'] == band
m = val[idx]
main_ax.plot(m['m5'], m['cadence'], dict_marker[key], alpha=alpha)
m5_exp = np.median(m['m5'])
cadence_ref = 3
if lims is not None:
fmt = {}
ll = [lims[zz][band] for zz in sorted_keys]
print('limits', ll)
cs = main_ax.contour(M5, DT, metric, ll, colors='k')
dict_target_snsim = Get_target(cs, sorted_keys, cadence_ref, m5_exp)
strs = ['$z=%3.1f$' % zz for zz in sorted_keys]
for l, s in zip(cs.levels, strs):
fmt[l] = s
main_ax.clabel(cs,
inline=True,
fmt=fmt,
fontsize=16,
use_clabeltext=True)
if lims_sncosmo is not None:
#llc = [lims_sncosmo[zz][band.split('::')[1]] for zz in sorted_keys]
llc = [lims_sncosmo[zz][band] for zz in sorted_keys]
b = [band] * len(llc)
print 'baba', band, llc, [zz for zz in sorted_keys
], lsstpg.flux_to_mag(llc, b)
csc = main_ax.contour(M5, DT, metric, llc, colors='b')
dict_target_sncosmo = Get_target(csc, sorted_keys, cadence_ref, m5_exp)
strs = ['$z=%3.1f$' % zz for zz in sorted_keys]
for l, s in zip(csc.levels, strs):
fmt[l] = s
main_ax.clabel(csc, inline=1, fmt=fmt, fontsize=16)
"""
t = target.get(band, None)
print target, t
if t is not None:
main_ax.plot(t[0], t[1],
color='r', marker='*',
markersize=15)
"""
main_ax.set_xlabel('$m_{5\sigma}$', fontsize=18)
main_ax.set_ylabel(r'Observer frame cadence $^{-1}$ [days]', fontsize=18)
main_ax.set_title('$%s$ - %s' % (band.split(':')[-1], simu_name),
fontsize=18)
main_ax.set_xlim(mag_range)
main_ax.set_ylim(dt_range)
main_ax.grid(1)
if save_it:
outdir = 'Plots_' + simu_name + '_' + thetype + '/Season_' + str(seas)
if seas == -1:
outdir = 'Plots_' + simu_name + '_' + thetype
Check_Dir(outdir)
plt.gcf().savefig(outdir + '/metric_' + band + '.png',
bbox_inches='tight')
else:
pstring = '{} - {} - {} - {}'.format(run, subrun, gate, phys_evt)
print(pstring)
evt = []
titles = []
if data_x is not None:
evt.append(data_x[counter])
titles.append('x view')
if data_u is not None:
evt.append(data_u[counter])
titles.append('u view')
if data_v is not None:
evt.append(data_v[counter])
titles.append('v view')
fig = pylab.figure(figsize=(9, 3))
gs = pylab.GridSpec(1, len(evt))
# print np.where(evt == np.max(evt))
# print np.max(evt)
for i in range(len(evt)):
ax = pylab.subplot(gs[i])
ax.axis('on')
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
# images are normalized such the max e-dep has val 1, independent
# of view, so set vmin, vmax here to keep matplotlib from
# normalizing each view on its own
minv = 0
cmap = 'jet'
if have_times:
minv = -1
cmap = 'bwr'
#sim.recompute_and_save_amplification_index()
sim.load_steady_scores()
# recompute gridness scores
#sim.compute_steady_scores(force_input_scores=True)
#sim.save_steady_scores()
#%%
### COMPARE GRIDNESS SCORE AND GRID-TUNING INDEX
fig=pl.figure(figsize=(8,3.5))
pl.subplots_adjust(top=0.95,right=0.95,bottom=0.15,left=0.15)
gs = pl.GridSpec(3,3,hspace=0.55,wspace=0.35)
ax_joint = fig.add_subplot(gs[1:3,0:2])
ax_marg_x = fig.add_subplot(gs[0,0:2],sharex=ax_joint)
ax_marg_y = fig.add_subplot(gs[1:3,2],sharey=ax_joint)
xlims=0,0.6
ylims=-0.3,1.7
ms=5
ax_joint.plot(sim.grid_tuning_out_inhib,sim.ri_scores,color='dodgerblue',marker='o',lw=0,alpha=0.5,ms=ms,mec='none')
ax_joint.plot(sim.grid_tuning_in,sim.he_scores,color='m',marker='o',lw=0,alpha=0.5,ms=ms,mec='none')
ax_joint.plot(sim.grid_tuning_out,sim.re_scores,color='black',marker='o',lw=0,alpha=0.5,ms=ms,mec='none')
ax_joint.set_xlabel('Grid tuning index')
ax_joint.set_ylabel('Gridness score')
def allplot(xb, yb, bins=30, fig=1, xlabel='x', ylabel='y'):
"""
Input:
X,Y : objects referring to the variables produced by PyMC that you want
to analyze. Example: X=M.theta, Y=M.slope.
Inherited from Tommy LE BLANC's code at astroplotlib|STSCI.
"""
#X,Y=xb.trace(),yb.trace()
X, Y = xb, yb
#pylab.rcParams.update({'font.size': fontsize})
fig = pylab.figure(fig)
pylab.clf()
gs = pylab.GridSpec(2,
2,
width_ratios=[3, 1],
height_ratios=[1, 3],
wspace=0.07,
hspace=0.07)
scat = pylab.subplot(gs[2])
histx = pylab.subplot(gs[0], sharex=scat)
histy = pylab.subplot(gs[3], sharey=scat)
#scat=fig.add_subplot(2,2,3)
#histx=fig.add_subplot(2,2,1, sharex=scat)
#histy=fig.add_subplot(2,2,4, sharey=scat)
# Scatter plot
scat.plot(X,
Y,
linestyle='none',
marker='o',
color='green',
mec='green',
alpha=.2,
zorder=-99)
gkde = scipy.stats.gaussian_kde([X, Y])
x, y = numpy.mgrid[X.min():X.max():(X.max() - X.min()) / 25.,
Y.min():Y.max():(Y.max() - Y.min()) / 25.]
z = numpy.array(gkde.evaluate([x.flatten(), y.flatten()])).reshape(x.shape)
scat.contour(x, y, z, linewidths=2)
scat.set_xlabel(xlabel)
scat.set_ylabel(ylabel)
# X-axis histogram
histx.hist(X, bins, histtype='stepfilled')
pylab.setp(histx.get_xticklabels(), visible=False) # no X label
#histx.xaxis.set_major_formatter(pylab.NullFormatter()) # no X label
# Y-axis histogram
histy.hist(Y, bins, histtype='stepfilled', orientation='horizontal')
pylab.setp(histy.get_yticklabels(), visible=False) # no Y label
#histy.yaxis.set_major_formatter(pylab.NullFormatter()) # no Y label
#pylab.minorticks_on()
#pylab.subplots_adjust(hspace=0.1)
#pylab.subplots_adjust(wspace=0.1)
pylab.draw()
pylab.show()
def plot_vars(cls,
var_dic,
suffix="evolution",
show=False,
save=True,
func=utils.plot):
"""plot variation/comparison/boxplots of all variables organized by categories
Args:
var_dic:
suffix: (Default value = "")
show(bool): If True, show the figure (Default value = True)
save(bool): If True, save the figure (Default value = False)
func: (Default value = plot)
Returns:
"""
fig = plt.figure()
grid = plt.GridSpec(2, 3)
for nb in range(len(GATES)):
gate = GATES[nb]
cls.plot_vars_gate(gate,
var_dic['{}__mdp'.format(gate)],
var_dic['{}__scale'.format(gate)],
var_dic['{}__tau'.format(gate)],
fig,
grid[nb], (nb % 3 == 0),
func=func)
cls.plot_vars_gate('h',
var_dic['h__mdp'],
var_dic['h__scale'],
var_dic['h__alpha'],
fig,
grid[5],
False,
func=func)
plt.tight_layout()
utils.save_show(show,
save,
name='{}Rates_{}'.format(utils.NEUR_DIR, suffix),
dpi=300)
fig = plt.figure()
grid = plt.GridSpec(1, 2)
subgrid = gridspec.GridSpecFromSubplotSpec(4, 1, grid[0], hspace=0.1)
for i, var in enumerate(CONDS):
ax = plt.Subplot(fig, subgrid[i])
func(ax, var_dic[var]) # )
ax.set_ylabel(var)
if (i == 0):
ax.set_title('Conductances')
fig.add_subplot(ax)
subgrid = gridspec.GridSpecFromSubplotSpec(4, 1, grid[1], hspace=0.1)
for i, var in enumerate(MEMB):
ax = plt.Subplot(fig, subgrid[i])
func(ax, var_dic[var]) # )
ax.set_ylabel(var)
if (i == 0):
ax.set_title('Membrane')
ax.yaxis.tick_right()
fig.add_subplot(ax)
plt.tight_layout()
utils.save_show(show,
save,
name='{}Membrane_{}'.format(utils.NEUR_DIR, suffix),
dpi=300)
plt.figure()
ax = plt.subplot(211)
func(ax, var_dic['rho_ca']) # , 'r')
plt.ylabel('Rho_ca')
plt.yscale('log')
ax = plt.subplot(212)
func(ax, var_dic['decay_ca']) # , 'b')
plt.ylabel('Decay_ca')
utils.save_show(show,
save,
name='{}CalciumPump_{}'.format(utils.NEUR_DIR, suffix),
dpi=300)
# SCRIPT
# # # # # # # # # #
# ax1 predefined
dt = 0.1 # ms per bin
T = 2e3 # in ms
t = np.arange(0, T, dt)
spktr = np.zeros(t.shape)
spktr[[4000, 5000, 6000, 7000, 14000]] = 1
fig1 = pl.figure(num=1, figsize=figsize, dpi=dpi)
lim1 = [0.24, 0.24, 0.72, 0.70]
gs = pl.GridSpec(5, 3, wspace=0.5, hspace=0.5)
fig2 = pl.figure(num=2, figsize=spiketrain_figsize, dpi=dpi)
if __name__ == "__main__":
import os, inspect
# FIGURE 1 PLOTTING
current_dir = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
figure1, figure1_spiketrain = plot()
figure1.savefig(current_dir + "/figures/Fig1_raw.pdf")
figure1_spiketrain.savefig(current_dir +
# ax_ts.set_ylim([0, 5.0])
# ax_ts.set_xlim([0, 6*np.pi])
# ax_ts.set_xticks([0, 2*np.pi, 4*np.pi, 6*np.pi])
# ax_ts.set_xticklabels([r'$0$', r'$2\pi$', r'$4\pi$',
# r'$6\pi$'])
#
# fig.tight_layout(**layout_pad)
# fig.savefig('cont_plot/cont' + "%03d" % (i,) + '.png',
# bbox_inches='tight')
# plt.close(fig)
# Desync movie
pulse_inds = range(len(ts_pre), len(ts_pre) + len(ts_pulse))
for i in xrange(ys_pert.shape[0]):
fig = plt.figure(dpi=70, figsize=(6, 2.2))
gs = plt.GridSpec(1, 3)
ax_ss = fig.add_subplot(gs[0])
ax_ss.plot(model.sol[:, 0], model.sol[:, 1], 'k')
ax_ss.plot(ys_pert[:i, :, 0].mean(1), ys_pert[:i, :, 1].mean(1), 'r--')
ax_ss.plot(ys_pert[i, :, 0], ys_pert[i, :, 1], 'go')
ax_ss.plot(ys_pert[i, :, 0].mean(0), ys_pert[i, :, 1].mean(0), 'ro')
ax_ss.set_xlim([0, 0.8])
ax_ss.set_xlabel('X')
ax_ss.set_ylabel('Y')
ax_ss.set_ylim([0, 5.0])
ax_ts = fig.add_subplot(gs[1:], sharey=ax_ss)
ax_ts.set_xlabel(r'$\hat{t}$')
ax_ts.plot(ts_pert[:i] - ts_pert[0], ys_pert[:i, :, 1].mean(1), 'r--')
ax_ts.plot(ts_pert[i] - ts_pert[0], ys_pert[i, :, 1].mean(0), 'ro')
ax_ts.set_ylim([0, 5.0])
pp.save_fig(sl.get_figures_path(),
fname,
exts=['png', 'svg'],
transparent=True)
#%%
### BUMP LOCATION AND AMPLITUDE WITH SWITCHING OFF FEED-FORWARD INPUTS (ATTRACTOR DYNAMICS)
fig = pl.figure(figsize=(8, 4))
time = np.arange(num_snaps) * sim.delta_snap * sim.dt
gs = pl.GridSpec(8,
1,
hspace=2.,
wspace=0.1,
bottom=0.15,
left=0.125,
right=0.99)
ax = fig.add_subplot(gs[0:2, 0])
for idx, curr_sim in enumerate(sims):
pl.plot(time, curr_sim.bump_peak_evo[0, :] / 30., color='salmon')
pl.plot(time, sims[4].bump_peak_evo[0, :] / 30., color='black', lw=1.5)
pp.custom_axes()
pl.yticks([0, 1])
pl.ylabel('Bump\nphase\n(vert.)')
pl.xlim([-0.1, walk_time + 0.1])
pl.gca().axes.get_xaxis().set_visible(False)
def plotting_from_dat(data, area='PFC'):
pdf = PdfPages('/Users/veronikasamborska/Desktop/' + area + 'from_dat.pdf')
cmap = palettable.scientific.sequential.Acton_3.mpl_colormap
dm = data['DM'][0]
fr = data['Data'][0]
neuron_ID = 0
for s, session in enumerate(dm):
# Get raw spike data across the task
firing_rate = fr[s]
DM = dm[s]
n_trials, n_neurons, n_times = firing_rate.shape
choices = DM[:, 1]
b_pokes = DM[:, 7]
a_pokes = DM[:, 6]
i_pokes = DM[:, 8]
reward = DM[:, 2]
task = DM[:, 5]
_t_1 = np.where(task == 1)[0]
_t_2 = np.where(task == 2)[0]
_t_3 = np.where(task == 3)[0]
x_points = [132, 232, 232, 332, 332, 432, 432, 532]
y_points = [2.8, 3.8, 1.8, 4.8, 0.8, 3.8, 1.8, 2.8]
global _4
global _2
global _7
global _1
global _9
global _3
global _8
global _6
_4 = [x_points[0], y_points[0]]
_2 = [x_points[1], y_points[1]]
_7 = [x_points[2], y_points[2]]
_1 = [x_points[3], y_points[3]]
_9 = [x_points[4], y_points[4]]
_3 = [x_points[5], y_points[5]]
_8 = [x_points[6], y_points[6]]
_6 = [x_points[7], y_points[7]]
a_1 = globals()['_' + str(int(a_pokes[_t_1][0]))]
a_2 = globals()['_' + str(int(a_pokes[_t_2][0]))]
a_3 = globals()['_' + str(int(a_pokes[_t_3][0]))]
b_1 = globals()['_' + str(int(b_pokes[_t_1][0]))]
b_2 = globals()['_' + str(int(b_pokes[_t_2][0]))]
b_3 = globals()['_' + str(int(b_pokes[_t_3][0]))]
i_1 = globals()['_' + str(int(i_pokes[_t_1][0]))]
i_2 = globals()['_' + str(int(i_pokes[_t_2][0]))]
i_3 = globals()['_' + str(int(i_pokes[_t_3][0]))]
ind_init = 25
ind_choice = 36
ind_reward = 42
a_rew_1_f = np.mean(
firing_rate[np.where((task == 1) & (choices == 1)
& (reward == 1))[0]], 0)
a_nrew_1_f = np.mean(
firing_rate[np.where((task == 1) & (choices == 1)
& (reward == 0))[0]], 0)
a_rew_2_f = np.mean(
firing_rate[np.where((task == 2) & (choices == 1)
& (reward == 1))[0]], 0)
a_nrew_2_f = np.mean(
firing_rate[np.where((task == 2) & (choices == 1)
& (reward == 0))[0]], 0)
a_rew_3_f = np.mean(
firing_rate[np.where((task == 3) & (choices == 1)
& (reward == 1))[0]], 0)
a_nrew_3_f = np.mean(
firing_rate[np.where((task == 3) & (choices == 1)
& (reward == 0))[0]], 0)
b_rew_1_f = np.mean(
firing_rate[np.where((task == 1) & (choices == 0)
& (reward == 1))[0]], 0)
b_nrew_1_f = np.mean(
firing_rate[np.where((task == 1) & (choices == 0)
& (reward == 0))[0]], 0)
b_rew_2_f = np.mean(
firing_rate[np.where((task == 2) & (choices == 0)
& (reward == 1))[0]], 0)
b_nrew_2_f = np.mean(
firing_rate[np.where((task == 2) & (choices == 0)
& (reward == 0))[0]], 0)
b_rew_3_f = np.mean(
firing_rate[np.where((task == 3) & (choices == 0)
& (reward == 1))[0]], 0)
b_nrew_3_f = np.mean(
firing_rate[np.where((task == 3) & (choices == 0)
& (reward == 0))[0]], 0)
i_rew_1_f = np.mean(
firing_rate[np.where((task == 1) & (reward == 1))[0]], 0)
i_nrew_1_f = np.mean(
firing_rate[np.where((task == 1) & (reward == 0))[0]], 0)
i_rew_2_f = np.mean(
firing_rate[np.where((task == 2) & (reward == 1))[0]], 0)
i_nrew_2_f = np.mean(
firing_rate[np.where((task == 2) & (reward == 0))[0]], 0)
i_rew_3_f = np.mean(
firing_rate[np.where((task == 3) & (reward == 1))[0]], 0)
i_nrew_3_f = np.mean(
firing_rate[np.where((task == 3) & (reward == 0))[0]], 0)
plt.ioff()
task_arrays = np.zeros(shape=(n_trials, 3))
task_arrays[:_t_2[0], 0] = 1
task_arrays[_t_2[0]:_t_3[0], 1] = 1
task_arrays[_t_3[0]:, 2] = 1
isl = wes.Royal2_5.mpl_colors
isl_1 = wes.Moonrise5_6.mpl_colors
vector_for_normalising = np.concatenate([a_rew_1_f,a_nrew_1_f,a_rew_2_f,a_nrew_2_f,\
a_rew_3_f,a_nrew_3_f,b_rew_1_f,b_nrew_1_f,b_rew_2_f,b_nrew_2_f,\
b_rew_3_f,b_nrew_3_f,\
i_rew_1_f,i_nrew_1_f,i_rew_2_f,i_nrew_2_f,\
i_rew_3_f,i_nrew_3_f], axis = 1)
normalised = vector_for_normalising / (
np.tile(np.max(vector_for_normalising, 1),
[vector_for_normalising.shape[1], 1]).T + .0000001)
a_rew_1_f = normalised[:, :n_times]
a_nrew_1_f = normalised[:, n_times:n_times * 2]
a_rew_2_f = normalised[:, n_times * 2:n_times * 3]
a_nrew_2_f = normalised[:, n_times * 3:n_times * 4]
a_rew_3_f = normalised[:, n_times * 4:n_times * 5]
a_nrew_3_f = normalised[:, n_times * 5:n_times * 6]
b_rew_1_f = normalised[:, n_times * 6:n_times * 7]
b_nrew_1_f = normalised[:, n_times * 7:n_times * 8]
b_rew_2_f = normalised[:, n_times * 8:n_times * 9]
b_nrew_2_f = normalised[:, n_times * 9:n_times * 10]
b_rew_3_f = normalised[:, n_times * 10:n_times * 11]
b_nrew_3_f = normalised[:, n_times * 11:n_times * 12]
i_rew_1_f = normalised[:, n_times * 12:n_times * 13]
i_nrew_1_f = normalised[:, n_times * 13:n_times * 14]
i_rew_2_f = normalised[:, n_times * 14:n_times * 15]
i_nrew_2_f = normalised[:, n_times * 15:n_times * 16]
i_rew_3_f = normalised[:, n_times * 16:n_times * 17]
i_nrew_3_f = normalised[:, n_times * 17:]
for neuron in range(n_neurons):
neuron_ID += 1
#Port Firing
fig = plt.figure(figsize=(8, 15))
grid = plt.GridSpec(2, 1, hspace=0.7, wspace=0.4)
fig.add_subplot(grid[0])
plt.scatter(x_points, y_points, s=100, c='black')
plt.plot(np.arange(a_1[0] - 30, a_1[0] + 33, 1),
a_rew_1_f[neuron] + a_1[1] + 1,
color=isl[0],
label='task 1 A')
plt.plot(np.arange(a_1[0] - 30, a_1[0] + 33, 1),
a_nrew_1_f[neuron] + a_1[1] + 1,
color=isl[0],
linestyle=':')
plt.plot(np.arange(a_2[0] - 30, a_2[0] + 33, 1),
a_rew_2_f[neuron] + a_2[1] + 1,
color=isl[1],
label='task 2 A')
plt.plot(np.arange(a_2[0] - 30, a_2[0] + 33, 1),
a_nrew_2_f[neuron] + a_2[1] + 1,
color=isl[1],
linestyle=':')
plt.plot(np.arange(a_3[0] - 30, a_3[0] + 33, 1),
a_rew_3_f[neuron] + a_3[1] + 1,
color=isl[2],
label='task 3 A')
plt.plot(np.arange(a_3[0] - 30, a_3[0] + 33, 1),
a_nrew_3_f[neuron] + a_3[1] + 1,
color=isl[2],
linestyle=':')
plt.plot(np.arange(b_1[0] - 30, b_1[0] + 33, 1),
b_rew_1_f[neuron] + b_1[1] + 1,
color=isl[0],
label='task 1 B')
plt.plot(np.arange(b_1[0] - 30, b_1[0] + 33, 1),
b_nrew_1_f[neuron] + b_1[1] + 1,
color=isl[0],
linestyle=':')
plt.plot(np.arange(b_2[0] - 30, b_2[0] + 33, 1),
b_rew_2_f[neuron] + b_2[1] + 1,
color=isl[1],
label='task 2 B')
plt.plot(np.arange(b_2[0] - 30, b_2[0] + 33, 1),
b_nrew_2_f[neuron] + b_2[1] + 1,
color=isl[1],
linestyle=':')
plt.plot(np.arange(b_3[0] - 30, b_3[0] + 33, 1),
b_rew_3_f[neuron] + b_3[1] + 1,
color=isl[2],
label='task 3 B')
plt.plot(np.arange(b_3[0] - 30, b_3[0] + 33, 1),
b_nrew_3_f[neuron] + b_3[1] + 1,
color=isl[2],
linestyle=':')
plt.plot(np.arange(i_1[0] - 30, i_1[0] + 33, 1),
i_rew_1_f[neuron] + i_1[1] + 1,
color=isl_1[0],
label='task 1 I')
plt.plot(np.arange(i_1[0] - 30, i_1[0] + 33, 1),
i_nrew_1_f[neuron] + i_1[1] + 1,
color=isl_1[0],
linestyle=':')
plt.plot(np.arange(i_2[0] - 30, i_2[0] + 33, 1),
i_rew_2_f[neuron] + i_2[1] + 1,
color=isl_1[1],
label='task 2 I')
plt.plot(np.arange(i_2[0] - 30, i_2[0] + 33, 1),
i_nrew_2_f[neuron] + i_2[1] + 1,
color=isl_1[1],
linestyle=':')
plt.plot(np.arange(i_3[0] - 30, i_3[0] + 33, 1),
i_rew_3_f[neuron] + i_3[1] + 1,
color=isl_1[2],
label='task 3 I')
plt.plot(np.arange(i_3[0] - 30, i_3[0] + 33, 1),
i_nrew_3_f[neuron] + i_3[1] + 1,
color=isl_1[2],
linestyle=':')
inds = [
a_1, b_1, b_1, b_2, b_2, b_3, b_3, i_1, i_1, i_2, i_2, i_3, i_3
]
for i, ind in enumerate(inds):
plt.vlines(np.arange(ind[0] - 30, ind[0] + 33, 1)[ind_init],
ymin=ind[1] + 1,
ymax=ind[1] + 2,
linestyle='--',
color='Grey',
linewidth=0.5)
plt.vlines(np.arange(ind[0] - 30, ind[0] + 33, 1)[ind_choice],
ymin=ind[1] + 1,
ymax=ind[1] + 2,
linestyle='--',
color='Black',
linewidth=0.5)
plt.vlines(np.arange(ind[0] - 30, ind[0] + 33, 1)[ind_reward],
ymin=ind[1] + 1,
ymax=ind[1] + 2,
linestyle='--',
color='Pink',
linewidth=0.5)
plt.legend()
# Pokes
plt.axis('off')
# Heatmap
fig.add_subplot(grid[1])
heatplot = firing_rate[:, neuron, :]
# normalised = (heatplot - np.min(heatplot,1)[:, None]) / (np.max(heatplot,1)[:, None]+1e-08 - np.min(heatplot,1)[:, None])
heatplot = heatplot / (
np.tile(np.max(heatplot, 1), [heatplot.shape[1], 1]).T + 1e-08)
heatplot_con = np.concatenate([heatplot, task_arrays], axis=1)
plt.imshow(heatplot_con, cmap=cmap, aspect='auto')
plt.xticks([ind_init, ind_choice, ind_reward], ('I', 'C', 'O'))
plt.title('{}'.format(neuron_ID))
pdf.savefig()
plt.clf()
pdf.close()
trackfilename = sys.argv[2]
trackarr = None
if trackfilename:
trackarr = pylab.load(trackfilename)
zsegs = [0, 1, 6, 2, 7, 3, 8, 4, 9, 5, 10]
desc = [
'upstream of target 1', 'target 1', 'between 1 and 2', 'target 2',
'between 2 and 3', 'target 3', 'between 3 and 4', 'target 4',
'between 4 and 5', 'target 5', 'downstream of target 5'
]
zdesc = dict(zip(zsegs, desc))
fig = pylab.figure(figsize=(15, 15))
gs = pylab.GridSpec(4, 3)
pur = pylab.zeros_like(arr)
for i in range(11):
pur[i, :] = arr[i, :] / arr.sum(axis=1)[i]
if trackarr is not None:
trackpur = pylab.zeros_like(trackarr)
for i in range(11):
trackpur[i, :] = trackarr[i, :] / trackarr.sum(axis=1)[i]
for i, v in enumerate(zsegs):
ax = pylab.subplot(gs[i])
ax.set_autoscaley_on(False)
ax.set_ylim([0.0, 1.0])
ax.set_ylabel('fraction of events')
ax.set_title('true ' + str(v) + ':' + zdesc[v], loc='right')
def run_single_tilt_angle(subtomogram, ang, offset, vol_size,
particle_position, particle_rotation,
particle_filename, particle_number, binning, img,
create_graphics, fsc_path, dimz, peak_border):
"""
To run a single tilt angle to allow for parallel computing
@param ang: the tilt angle
@type ang: int
@param subtomogram: the filename of the subtomogram
@type subtomogram: str
@param offset: the offset used (x,y,z)
@type offset: list(int, int, int)
@param vol_size: the size of the volume to be reconstructed (in pixels)
@type vol_size: int
@param particle_position: the position of the particle in vector format,
as given by particle.pickPosition().toVector()
@type particle_position: tuple
@param particle_rotation: the rotation of the particle (Z1/phi, X/the, Z2/psi)
@type particle_rotation: tuple
@param particle_filename: the filename of the particle, as given by particle.getfilename()
@type particle_filename: str
@param particle_number: the number of the particle, to allow for unique mapping
@type particle_number: int
@param binning: the binning factor used
@type binning: int
@param img: the filename of the projection to be used
@type img: str
@param create_graphics: to flag if images should be created for human inspection of the work done
@type create_graphics: bool
@return: the newly found positions of the particle, as a list in the LOCAL_ALIGNMENT_RESULTS format
@returntype: list
"""
from pytom.reconstruction.reconstructionFunctions import alignImageUsingAlignmentResultFile
from pytom.tompy.transform import rotate3d, rotate_axis
from pytom.gui.guiFunctions import datatypeAR, loadstar
import numpy as np
from math import cos, sin, pi, sqrt
from pytom.tompy.tools import create_circle
from pytom.tompy.transform import cut_from_projection
from pytom.tompy.filter import applyFourierFilterFull, bandpass_circle
from pytom.tompy.io import read, write
from pytom.tompy.correlation import meanUnderMask, stdUnderMask
import os
import time
t = time.time()
# print(particle_filename, ang)
# Filter using FSC
fsc_mask = None
k = 0.01
subtomogram = read(subtomogram) * read(
'/data/gijsvds/ctem/05_Subtomogram_Analysis/Alignment/GLocal/mask_200_75_5.mrc'
)
import os
from pytom.tompy.filter import filter_volume_by_profile, profile2FourierVol
fsc_path = ''
if os.path.isfile(fsc_path):
profile = [line.split()[0] for line in open(fsc_path, 'r').readlines()]
fsc_mask3d = profile2FourierVol(profile, subtomogram.shape)
subtomogram = applyFourierFilterFull(subtomogram, fsc_mask3d)
# Cross correlate the templat
# e and patch, this should give the pixel shift it is after
from pytom.tompy.correlation import nXcf
# Get template
# First rotate the template towards orientation of the particle, then to the tilt angle
img = read(img)
rotated1 = rotate3d(subtomogram,
phi=particle_rotation[0],
the=particle_rotation[1],
psi=particle_rotation[2])
rotated2 = rotate_axis(
rotated1, -ang,
'y') # SWITCHED TO ROTATE AXIS AND ANGLE *-1 THIS IS AN ATTEMPT
template = rotated2.sum(axis=2)
# write('pp1_template.mrc', template)
# img = read(img)
try:
patch, xx, yy = cut_patch(img,
ang,
particle_position,
dimz=dimz,
vol_size=vol_size,
binning=binning)
mask2d = create_circle(patch.shape, radius=75, sigma=5, num_sigma=2)
patch *= mask2d
# write('pp1_patch.mrc', patch)
if os.path.isfile(fsc_path):
profile = [
line.split()[0] for line in open(fsc_path, 'r').readlines()
]
fsc_mask2d = profile2FourierVol(profile, patch.shape)
patch = applyFourierFilterFull(patch, fsc_mask2d)
template = normalize_image(template, mask2d, mask2d.sum())
patch = normalize_image(patch, mask2d, mask2d.sum())
if 1:
ff = xp.ones_like(patch)
else:
ff = bandpass_circle(patch.squeeze(), 6, 25, 3)
ccf = normalised_cross_correlation(template, patch, ff)
points2d1 = find_sub_pixel_max_value_2d(ccf.copy(),
ignore_border=peak_border)
points2d2 = find_sub_pixel_max_value(ccf.copy(),
ignore_border=peak_border)
points2d = list(points2d2[:2]) + [points2d1[-1]]
x_diff = points2d[0] - vol_size / 2
y_diff = points2d[1] - vol_size / 2
dist = sqrt(x_diff**2 + y_diff**2)
#rx, ry, ii, oox, ooy, x2, y2 = find_sub_pixel_max_value_voltools(ccf.copy())
#print(rx,ry, x_diff, y_diff, x2, y2, oox, ooy, ii.shape)
#print(f'{particle_number:3d} {ang:5.1f}, {dist:5.2f} {x_diff} {y_diff} {ccf.max()}')
if create_graphics:
rx, ry, ii, oox, ooy, x2, y2 = find_sub_pixel_max_value_voltools(
ccf.copy(), k=k)
print(rx, ry)
from scipy.ndimage.filters import gaussian_filter
# Create an image to display and/or test the inner workings of the algorithm
points = find_sub_pixel_max_value(ccf, ignore_border=peak_border)
nx, ny, nz = particle_position
nx += x_diff
ny += y_diff
npatch = cut_from_projection(
img.squeeze(), [xx + x_diff, yy + y_diff],
[vol_size, vol_size]
) # img.sum(axis=2)[int(xx+x_diff-v):int(xx+x_diff+v), int(yy+y_diff-v):int(yy+y_diff+v)] #
npatch = normalize_image(npatch, mask2d, mask2d.sum())
nccf = normalised_cross_correlation(template, npatch.squeeze(), ff)
npoints = find_sub_pixel_max_value(nccf, ignore_border=peak_border)
npoints2d = find_sub_pixel_max_value_2d(nccf,
ignore_border=peak_border)
#rx, ry = abs(xp.array(npoints2d[:2]) - 100)
from scipy.ndimage.filters import gaussian_filter
# Create an image to display and/or test the inner workings of the algorithm
points = find_sub_pixel_max_value(ccf, ignore_border=peak_border)
nx, ny, nz = particle_position
nx += x_diff
ny += y_diff
npatch = cut_from_projection(
img.squeeze(), [xx + x_diff, yy + y_diff],
[vol_size, vol_size]
) # img.sum(axis=2)[int(xx+x_diff-v):int(xx+x_diff+v), int(yy+y_diff-v):int(yy+y_diff+v)] #
npatch = normalize_image(npatch, mask2d, mask2d.sum())
nccf = normalised_cross_correlation(template, npatch.squeeze(), ff)
npoints = find_sub_pixel_max_value(nccf, ignore_border=peak_border)
npoints2d = find_sub_pixel_max_value_2d(nccf,
ignore_border=peak_border)
#rx, ry = abs(xp.array(npoints2d[:2]) - 100)
m_style = dict(color='tab:blue',
linestyle=':',
marker='o',
markersize=5,
markerfacecoloralt='tab:red')
m_style_alt = dict(color='tab:red',
linestyle=':',
marker='o',
markersize=5,
markerfacecoloralt='tab:orange')
grid = pp.GridSpec(3,
3,
wspace=0,
hspace=0.35,
left=0.05,
right=0.95,
top=0.90,
bottom=0.05)
ax_0_0 = pp.subplot(grid[0, 0])
ax_0_1 = pp.subplot(grid[0, 1])
ax_0_2 = pp.subplot(grid[0, 2])
ax_1_0 = pp.subplot(grid[1, 0])
ax_1_1 = pp.subplot(grid[1, 1])
ax_1_2 = pp.subplot(grid[1, 2])
ax_2_0 = pp.subplot(grid[2, 0])
ax_2_1 = pp.subplot(grid[2, 1])
ax_2_2 = pp.subplot(grid[2, 2])
ax_0_0.axis('off')
ax_0_1.axis('off')
ax_0_2.axis('off')
ax_1_0.axis('off')
ax_1_1.axis('off')
ax_1_2.axis('off')
ax_2_0.axis('off')
ax_2_1.axis('off')
ax_2_2.axis('off')
axis_title(ax_0_0, "Cutout")
ax_0_0.imshow(applyFourierFilterFull(patch, xp.fft.fftshift(ff)))
axis_title(ax_0_1, "Template")
ax_0_1.imshow(template)
axis_title(ax_0_2, "Shifted Cutout\n(based on cross correlation)")
ax_0_2.imshow(npatch.squeeze())
axis_title(ax_1_0, u"Cross correlation\ncutout × template")
ax_1_0.imshow(ccf)
ax_1_0.plot([points[1]], [points[0]], fillstyle='none', **m_style)
ax_1_0.plot([points2d[1]], [points2d[0]],
fillstyle='none',
**m_style_alt)
ax_1_0.plot([vol_size / 2], [vol_size / 2], ",k")
ax_1_1.text(
0.5,
0.8,
"Red: 2D spline interpolation\nx: {:f}\ny: {:f}\nBlue: 1D spline interpolation\nx: {:f}\ny: {:f}"
"\nBlack: center".format(x_diff, y_diff,
points[0] - vol_size / 2,
points[1] - vol_size / 2),
fontsize=8,
horizontalalignment='center',
verticalalignment='center',
transform=ax_1_1.transAxes)
axis_title(ax_1_2, u"Cross correlation\nshifted cutout × template")
ax_1_2.imshow(nccf)
ax_1_2.plot([npoints[0]], [npoints[1]],
fillstyle='none',
**m_style)
ax_1_2.plot([npoints2d[0]], [npoints2d[1]],
fillstyle='none',
**m_style_alt)
ax_1_2.plot([vol_size / 2], [vol_size / 2], ",k")
axis_title(ax_2_0, u"Zoom into red peak\nin CC cutout × template")
d = 10
points2d = list(xp.unravel_index(ccf.argmax(),
ccf.shape)) + [points2d[-1]]
peak = ccf[int(points2d[0]) - d:int(points2d[0]) + d,
int(points2d[1]) - d:int(points2d[1] + d)]
ax_2_0.imshow(peak)
axis_title(
ax_2_1,
u"Zoom into red peak\nin CC cutout × template\ninterpolated")
ax_2_1.imshow(ii)
ax_2_1.plot([y2 + (y_diff - ry) / k], [x2 + (x_diff - rx) / k],
fillstyle='none',
**m_style)
ax_2_1.plot([y2], [x2], fillstyle='none', **m_style_alt)
axis_title(ax_2_2, u"Cutout\nGaussian filter σ3")
import scipy
ax_2_2.imshow(scipy.ndimage.gaussian_filter(patch, 3))
pp.savefig(
"../Images/test/polish_particle_{:04d}_tiltimage_{:05.2f}_shift_{:.1f}.png"
.format(particle_number, ang, dist))
del img
except Exception as e:
print(e)
x_diff = y_diff = 0
print(
f'{particle_number:3d} {ang:4d} {x_diff:5.2f} {y_diff:5.2f} {points2d1[0]-patch.shape[0]//2:.2f} {points2d1[1]-patch.shape[0]//2:.2f} {time.time()-t:5.3f}'
)
return particle_number, x_diff, y_diff, ang, 0, 0, particle_filename
py.xlabel('$x$ ($\mu$m)')
py.ylabel('$y$ ($\mu$m)')
#py.figure(3)
# Animation call
# =============================================================================
print('Processing animation ... \n')
fig = py.figure()
# Define a 2 x 2 grid
gs = py.GridSpec(2,2) # 2 rows, 2 columns
# Define 3 subplots. First one spanning both rows of column 1, the rest two taking the ramining 2 subplots
ax1 = fig.add_subplot(gs[:,0],aspect = 1) # xy plane (column left, both rows)
ax2 = fig.add_subplot(gs[0,1],aspect = 1) # yz plane (column right, top row)
ax3 = fig.add_subplot(gs[1,1],aspect = 1) # xz plane (column right, bottoom row)
# Set figure dpi and size
fig.set_dpi(100)
fig.set_size_inches(14, 6)
# Lower and upper limit of the axes of the three subplots
def jointplotx(X,
Y,
xlabel=None,
ylabel=None,
binsim=40,
binsh=20,
binscon=15):
"""
Plots the joint distribution of posteriors for X1 and X2, including the 1D
histograms showing the median and standard deviations. Uses simple method
for drawing the confidence contours compared to jointplot (which is wrong).
The work that went in creating this method is shown, step by step, in
the ipython notebook "error contours.ipynb". Sources of inspiration:
- http://python4mpia.github.io/intro/quick-tour.html
Usage:
>>> jointplot(M.rtr.trace(),M.mdot.trace(),xlabel='$\log \ r_{\\rm tr}$', ylabel='$\log \ \dot{m}$')
"""
# Generates 2D histogram for image
histt, xt, yt = numpy.histogram2d(X,
Y,
bins=[binsim, binsim],
normed=False)
histt = numpy.transpose(
histt) # Beware: numpy switches axes, so switch back.
# assigns correct proportions to subplots
fig = pylab.figure()
gs = pylab.GridSpec(2,
2,
width_ratios=[3, 1],
height_ratios=[1, 3],
wspace=0.001,
hspace=0.001)
con = pylab.subplot(gs[2])
histx = pylab.subplot(gs[0], sharex=con)
histy = pylab.subplot(gs[3], sharey=con)
# Image
con.imshow(histt,
extent=[xt[0], xt[-1], yt[0], yt[-1]],
origin='lower',
cmap=pylab.cm.gray_r,
aspect='auto')
# Overplot with error contours 1,2 sigma
# Contour plot
histdata, x, y = numpy.histogram2d(X,
Y,
bins=[binscon, binscon],
normed=False)
histdata = numpy.transpose(
histdata) # Beware: numpy switches axes, so switch back.
pmax = histdata.max()
cs = con.contour(histdata,
levels=[0.68 * pmax, 0.05 * pmax],
extent=[x[0], x[-1], y[0], y[-1]],
colors=['black', 'blue'])
# use dictionary in order to assign your own labels to the contours.
#fmtdict = {s[0]:r'$1\sigma$',s[1]:r'$2\sigma$'}
#con.clabel(cs, fmt=fmtdict, inline=True, fontsize=20)
if xlabel != None: con.set_xlabel(xlabel)
if ylabel != None: con.set_ylabel(ylabel)
# X-axis histogram
histx.hist(X, binsh, histtype='stepfilled', facecolor='lightblue')
pylab.setp(histx.get_xticklabels(), visible=False) # no X label
pylab.setp(histx.get_yticklabels(), visible=False) # no Y label
# Vertical lines with median and 1sigma confidence
yax = histx.set_ylim()
histx.plot([numpy.median(X), numpy.median(X)], [yax[0], yax[1]],
'k-',
linewidth=2) # median
xsd = scipy.stats.scoreatpercentile(X, [15.87, 84.13])
histx.plot([xsd[0], xsd[0]], [yax[0], yax[1]], 'k--') # -1sd
histx.plot([xsd[-1], xsd[-1]], [yax[0], yax[1]], 'k--') # +1sd
# Y-axis histogram
histy.hist(Y,
binsh,
histtype='stepfilled',
orientation='horizontal',
facecolor='lightyellow')
pylab.setp(histy.get_yticklabels(), visible=False) # no Y label
pylab.setp(histy.get_xticklabels(), visible=False) # no X label
# Vertical lines with median and 1sigma confidence
xax = histy.set_xlim()
histy.plot([xax[0], xax[1]],
[numpy.median(Y), numpy.median(Y)],
'k-',
linewidth=2) # median
ysd = scipy.stats.scoreatpercentile(Y, [15.87, 84.13])
histy.plot([xax[0], xax[1]], [ysd[0], ysd[0]], 'k--') # -1sd
histy.plot([xax[0], xax[1]], [ysd[-1], ysd[-1]], 'k--') # +1sd