def __getNormalization(self):
if self._isLogScaleEnabled():
return lognorm()
elif self._isNormalizedScaleEnabled():
return norm(vmin=0.0, vmax=1.0)
return None
def draw_shotchart(xs, ys, pct):
from matplotlib import pyplot as plot
from matplotlib.colors import Normalize as norm
plot.scatter(xs,
ys,
c=pct,
marker='s',
cmap=plot.cm.jet,
norm=norm(vmin=0.25, vmax=0.85),
s=100)
plot.show()
def create_shotchart_plot(tups):
from matplotlib import pyplot as plot
from matplotlib.colors import Normalize as norm
for (x, y, pct, op) in tups:
plot.scatter(x,
y,
c=pct,
alpha=op,
marker='s',
cmap=plot.cm.jet,
norm=norm(vmin=0.25, vmax=0.7),
s=50)
return plot
def addSegmentedColorbar(ax,
colors,
vmin,
vmax,
label,
logflag=0,
fontsize=16,
cmap_number=0,
tick_tuple=None):
## find figure sizes
fig = ax.get_figure()
cur_height, cur_width = fig.get_size_inches() * fig.dpi
offset = cmap_number * (150 / cur_width)
## make the colorbar axes
ax1 = fig.add_axes([0.95 + offset, 0.125, 15. / cur_width, 0.75])
## setup segmented colormap
cmap = matplotlib.colors.ListedColormap(colors)
cmap.set_over(colors[-1])
cmap.set_under(colors[0])
if logflag:
ticks = 10**np.linspace(np.log10(vmin), np.log10(vmax),
len(cmap.colors) + 1)[1:-1]
norm = matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax)
tick_labels = [r"$10^{%.2f}$" % tick for tick in np.log10(ticks)]
else:
ticks = np.linspace(vmin, vmax, len(cmap.colors) + 1)[1:-1]
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
tick_labels = ticks
## allow explicit tick placement and labelling
if tick_tuple is not None:
ticks, tick_labels = tick_tuple
cb = matplotlib.colorbar.ColorbarBase(ax1,
cmap=cmap,
norm=norm,
extend='both',
extendfrac=.05,
extendrect=False,
ticks=ticks,
orientation='vertical')
cb.set_label(label, fontsize=fontsize)
cb.set_ticklabels(tick_labels)
cb.ax.tick_params(labelsize=fontsize - 2)
return lambda x: cmap(norm(x))
def addColorbar(ax,
cmap,
vmin,
vmax,
label,
logflag=0,
fontsize=16,
cmap_number=0,
tick_tuple=None):
if logflag:
from matplotlib.colors import LogNorm as norm
ticks = np.linspace(np.log10(vmin), np.log10(vmax), 5, endpoint=True)
tick_labels = [r"$10^{%.1f}$" % tick for tick in ticks]
ticks = 10**ticks
else:
from matplotlib.colors import Normalize as norm
ticks = np.linspace(vmin, vmax, 5, endpoint=True)
tick_labels = ticks
if tick_tuple is not None:
ticks, tick_labels = tick_tuple
fig = ax.get_figure()
## x,y of bottom left corner, width,height in percentage of figure size
## matches the default aspect ratio of matplotlib
cur_size = fig.get_size_inches() * fig.dpi
cur_height = cur_size[1]
cur_width = cur_size[0]
offset = 0.00 + cmap_number * (25 / cur_width + 50 / cur_width)
ax1 = fig.add_axes([0.95 + offset, 0.125, 25. / cur_width, 0.75])
cb1 = matplotlib.colorbar.ColorbarBase(ax1,
cmap=cmap,
extend='both',
extendfrac=0.05,
norm=norm(vmin=vmin, vmax=vmax),
orientation='vertical')
cb1.set_label(label, fontsize=fontsize)
cb1.set_ticks(ticks)
cb1.set_ticklabels(tick_labels)
cb1.ax.tick_params(labelsize=fontsize - 2)
return cb1, ax1
def plott(x, y, Z):
"""Function for 3D plotting,
edited from https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html
"""
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x, y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False, norm=norm(vmin=0, vmax=0.3))
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.view_init(40, 92)
ax.set_zlim(0,0.5)
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
def create_shotchart_png(tups):
"""
returns a stringIO object that stores the binary PNG data for a plot
"""
from matplotlib import pyplot as plot
from matplotlib.colors import Normalize as norm
for (x, y, pct, op) in tups:
plot.scatter(x,
y,
c=pct,
alpha=op,
marker='s',
cmap=plot.cm.jet,
norm=norm(vmin=0.25, vmax=0.7),
s=50,
linewidths=0)
out = StringIO.StringIO()
plot.savefig(out, ext='png')
plot.close()
return out
def render(self): # render the image and centroid coordinate
# load the region of interest within the image
img = self.image[int(self.f),
self.lims()['ylims'][0]:self.lims()['ylims'][1],
self.lims()['xlims'][0]:self.lims()['xlims'][1]]
# show the image, aximg is outputed to refresh its content
# in order to navigate faster within the frames
aximg = plt.imshow(
img,
norm=norm(vmin=np.amin(self.image),
vmax=(np.amax(self.image) - self.saturation *
(np.amax(self.image) - np.amin(self.image))
)), # intensity normalization
cmap=self.cmap)
# plot centroid
axcentroid = plt.plot(self.centroid()[0] - self.lims()['xlims'][0],
self.centroid()[1] - self.lims()['ylims'][0],
marker=self.marker,
markersize=self.markersize,
color='red',
markerfacecolor='none')
# graphical parameters
plt.xlabel("Pixels")
plt.ylabel("Pixels")
plt.title( self.trajectory.annotations()[ 'file' ] + ' ' + '\n' + \
'trajectory ' + str( self.j + 1 ) + '/' + str( len( self.tlist ) ) + '; ' + 'frame ' + str( self.f - self.fmin ) + '/' + str( self.fmax - self.fmin ) + '; ' + \
r'$r=$' + str( self.r ) + '; ' + \
r'$s=$' + str( round( self.saturation , 2 ) ) )
self.canvas.draw()
return [aximg, axcentroid[0]]
def addColorbar(ax,
cmap,
vmin,
vmax,
label,
logflag=0,
fontsize=12,
cmap_number=0,
tick_tuple=None,
horizontal=False,
span_full_figure=True):
if logflag:
from matplotlib.colors import LogNorm as norm
ticks = np.linspace(np.log10(vmin), np.log10(vmax), 5, endpoint=True)
ticks = 10**ticks
tick_labels = [my_log_formatter(tick, None) for tick in ticks]
else:
from matplotlib.colors import Normalize as norm
ticks = np.linspace(vmin, vmax, 5, endpoint=True)
tick_labels = ["%.2f" % tick for tick in ticks]
if tick_tuple is not None:
ticks, tick_labels = tick_tuple
fig = ax.get_figure()
## x,y of bottom left corner, width,height in percentage of figure size
## matches the default aspect ratio of matplotlib
cur_size = fig.get_size_inches() * fig.dpi
bbox = ax.get_position()
extents = bbox.extents
offset = 10
if span_full_figure:
for ax in fig.get_axes():
bbox = ax.get_position()
these_extents = bbox.extents
for i in range(2):
if these_extents[i] < extents[i]:
extents[i] = these_extents[i]
for i in range(2, 4):
if these_extents[i] > extents[i]:
extents[i] = these_extents[i]
height = extents[3] - extents[1]
width = extents[2] - extents[0]
else:
height = bbox.height
width = bbox.width
fig_x0, fig_y0, fig_x1, fig_y1 = extents
if not horizontal:
thickness = 20. / cur_size[0] * fig.dpi / 100
ax1 = fig.add_axes(
[fig_x1 + offset / cur_size[0], fig_y0, thickness, height])
else:
xlabel = ax.xaxis.get_label()
if xlabel.get_text() != '':
print(
"addColorbar does not support finding xaxis text, this will look bad"
)
thickness = 15. / cur_size[1] * fig.dpi / 100
ax1 = fig.add_axes([
fig_x0, fig_y0 - thickness - offset / cur_size[1], width, thickness
])
if type(cmap) == str:
cmap = plt.get_cmap(cmap)
cb1 = matplotlib.colorbar.ColorbarBase(
ax1,
cmap=cmap,
extend='both',
extendfrac=0.05,
norm=norm(vmin=vmin, vmax=vmax),
orientation='vertical' if not horizontal else 'horizontal')
cb1.set_label(label, fontsize=fontsize)
cb1.set_ticks(ticks)
if tick_labels is not None:
cb1.set_ticklabels(tick_labels)
cb1.ax.tick_params(labelsize=fontsize)
return cb1, ax1