def demo_fixed_size_axes():
fig1 = plt.figure(1, (6, 6))
# The first items are for padding and the second items are for the axes.
# sizes are in inch.
h = [Size.Fixed(1.0), Size.Fixed(4.5)]
v = [Size.Fixed(0.7), Size.Fixed(5.)]
divider = Divider(fig1, (0.0, 0.0, 1., 1.), h, v, aspect=False)
# the width and height of the rectangle is ignored.
ax = LocatableAxes(fig1, divider.get_position())
ax.set_axes_locator(divider.new_locator(nx=1, ny=1))
fig1.add_axes(ax)
ax.plot([1,2,3])
def subplot_array(self, hsize, vsize=(1.0, ), figsize=(10, 10)):
""" Use the axes_divider module to make a single row of plots
hsize : list of floats
horizontal spacing: alternates Scaled for plot, Fixed for between plots
vsize : list of floats
vertical spacing
ref: http://matplotlib.org/mpl_toolkits/axes_grid/users/axes_divider.html
"""
nx = (len(hsize) + 1) / 2
ny = (len(vsize) + 1) / 2
fig, axx = plt.subplots(
ny, nx, squeeze=False,
figsize=figsize) # just to make the axes, will move them
sizer = lambda x, i: axes_size.Scaled(
x) if i % 2 == 0 else axes_size.Fixed(x)
horiz = [sizer(h, i) for i, h in enumerate(hsize)]
vert = [sizer(v, i) for i, v in enumerate(vsize)]
divider = Divider(fig, (0.1, 0.1, 0.8, 0.8), horiz, vert, aspect=False)
for i, ax in enumerate(axx.flatten()):
iy = i // nx
ix = i % nx
ax.set_axes_locator(divider.new_locator(nx=2 * ix, ny=2 * iy))
return fig, axx
import mpl_toolkits.axes_grid.axes_size as Size
from mpl_toolkits.axes_grid import Divider
import matplotlib.pyplot as plt
fig1 = plt.figure(1, (5.5, 4.))
# the rect parameter will be ignore as we will set axes_locator
rect = (0.1, 0.1, 0.8, 0.8)
ax = [fig1.add_axes(rect, label="%d" % i) for i in range(4)]
horiz = [Size.Scaled(1.5), Size.Fixed(.5), Size.Scaled(1.), Size.Scaled(.5)]
vert = [Size.Scaled(1.), Size.Fixed(.5), Size.Scaled(1.5)]
# divide the axes rectangle into grid whose size is specified by horiz * vert
divider = Divider(fig1, rect, horiz, vert, aspect=False)
ax[0].set_axes_locator(divider.new_locator(nx=0, ny=0))
ax[1].set_axes_locator(divider.new_locator(nx=0, ny=2))
ax[2].set_axes_locator(divider.new_locator(nx=2, ny=2))
ax[3].set_axes_locator(divider.new_locator(nx=2, nx1=4, ny=0))
for ax1 in ax:
plt.setp(ax1.get_xticklabels() + ax1.get_yticklabels(), visible=False)
plt.draw()
plt.show()
def redraw(self):
if len(self.input) == 0:
self.warning("Empty input")
return
fast_redraw = self.name in self.pp.get_figlabels()
fig = self.pp.figure(self.name)
axes = fig.add_subplot(111)
if not fast_redraw:
self.draw_grid(axes)
else:
while len(axes.texts):
axes.texts[0].remove()
# Draw the inner hexagons with text
all_patches = [list() for _ in range(len(self.fitness_by_label))]
hits_max = numpy.max(self.input)
if hits_max == 0:
hits_max = 1
reversed_result = {}
for label, neurons in enumerate(self.result):
for neuron in neurons:
reversed_result[neuron] = label
# Add hexagons one by one
for y in range(self.height):
for x in range(self.width):
neuron = y * self.width + x
fitness = self.fitness_by_neuron[neuron]
number = self.input[y * self.width + x]
if numpy.sqrt(number / hits_max) <= \
KohonenHits.SIZE_TEXT_THRESHOLD:
fitness = 0
try:
label = reversed_result[neuron]
except KeyError:
continue
patches = all_patches[label]
self._add_hexagon(axes, patches, x, y, fitness)
if fast_redraw:
axes.collections = [axes.collections[-1]]
else:
legend_patches = []
cmap = getattr(self.cm, self.cmap)
for index, patches in enumerate(all_patches):
if len(patches) == 0:
continue
facecolor = cmap(index * cmap.N // len(all_patches))
col = self.matplotlib.collections.PatchCollection(
patches, edgecolors='none', facecolors=facecolor)
axes.add_collection(col)
if not fast_redraw:
legend_patches.append(
self.matplotlib.patches.Patch(
color=facecolor,
label="%d - %.2f" %
(index, self.fitness_by_label[index])))
# Make the grid to be drawn last
axes.collections = axes.collections[1:] + [axes.collections[0]]
if not fast_redraw:
legend = axes.legend(handles=legend_patches,
loc='upper left',
bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
title="Fitness: %.2f" % self.fitness)
fig.tight_layout()
# The legend becomes truncated, but it should not
# Measure the legend width to fix buggy matplotlib layout
# Without drawing, window extent equals to 1
if legend is not None:
legend.draw(fig.canvas.get_renderer())
bbox = legend.get_window_extent()
bbox = bbox.transformed(fig.dpi_scale_trans.inverted())
from mpl_toolkits.axes_grid import Divider
from mpl_toolkits.axes_grid.axes_size import Fixed, Scaled
divider = Divider(
fig, (0.05, 0.05, 0.9, 0.9),
(Scaled(1.0), Fixed(bbox.width), Scaled(0.05)),
(Scaled(1), Fixed(0)))
axes.set_axes_locator(divider.new_locator(0, 0))
else:
# Modify legend title and labels
legend = axes.get_legend()
if legend is not None:
legend.set_title("Fitness: %.2f" % self.fitness)
for index, text in enumerate(legend.get_texts()):
text.set_text("%d - %.2f" %
(index, self.fitness_by_label[index]))
self.show_figure(fig)
fig.canvas.draw()
return fig
def static_plot_grid_hist_selections(lst_det_left, lst_det_right, selection,
fname_det):
"""
:param lst_det_left:
:param lst_det_right:
:param selection:
:param fname_det:
"""
# Plot starts here:
cms = matplotlib.cm
color_map_left = cms.Blues
color_map_right = cms.RdPu
if fname_det:
f1 = plt.figure(1, (23, 13), dpi=1200)
else:
f1 = plt.figure(1, (15, 8))
vel_range = range(-90, 70)
f1.clf()
# the rect parameter is ignored as we set the axes_locator
rect = (0.05, 0.07, 0.9, 0.87)
f1ax = [f1.add_axes(rect, label="%d" % i) for i in range(7)]
horiz = [
Size.Scaled(5.),
Size.Fixed(1.0),
Size.Scaled(1.5),
Size.Fixed(1.0),
Size.Scaled(2.)
]
vert = [
Size.Scaled(1.),
Size.Fixed(0.5),
Size.Scaled(1.),
Size.Fixed(0.5),
Size.Scaled(1.),
Size.Fixed(0.5),
Size.Scaled(1.)
]
# divide the axes rectangle into grid whose size is specified by horiz * vert
divider = Divider(f1, rect, horiz, vert, aspect=False)
f1ax[0].set_axes_locator(divider.new_locator(nx=0, ny=0, ny1=7))
f1ax[1].set_axes_locator(divider.new_locator(nx=2, ny=0))
f1ax[2].set_axes_locator(divider.new_locator(nx=2, ny=2))
f1ax[3].set_axes_locator(divider.new_locator(nx=2, ny=4))
f1ax[4].set_axes_locator(divider.new_locator(nx=2, ny=6))
f1ax[5].set_axes_locator(divider.new_locator(nx=4, ny=0, ny1=3))
f1ax[6].set_axes_locator(divider.new_locator(nx=4, ny=4, ny1=7))
f1ax[0].axis([-40, 100, -80, 80])
f1ax[5].axis([-140, 140, 0, 100])
f1ax[6].axis([-140, 140, -100, 80])
f1ax[0].grid(True)
f1ax[1].grid(True)
f1ax[2].grid(True)
f1ax[3].grid(True)
f1ax[4].grid(True)
f1ax[5].grid(True)
f1ax[6].grid(True)
number_of_dets_left = 0
number_of_dets_left_processed = 0
number_of_dets_right = 0
number_of_dets_right_processed = 0
# Left radar plot
if lst_det_left:
LR_data = lst_det_left.get_array_detections_selected(
selection=selection)
if LR_data["mcc"].any():
LR_data_exists = True
f1ax[2].hist(LR_data["rvelocity"],
vel_range,
color=color_map_left(0.4),
normed=1)
number_of_dets_left = np.size(LR_data["mcc"])
if selection["beam_tp"].count(0):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [0]
LR0_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR0_data["rvelocity"],
vel_range,
color=color_map_left(0.2),
normed=1,
label='beam 0')
f1ax[0].plot(LR0_data["x"],
LR0_data["y"],
color=color_map_left(0.2),
marker='o',
ls='None',
label='Left RDR, beam 0')
f1ax[6].plot(-180 * LR0_data["razimuth"] / np.pi,
LR0_data["rvelocity"],
color=color_map_left(0.2),
marker='o',
ls='None',
label='Left RDR, beam 0')
f1ax[5].plot(-180 * LR0_data["razimuth"] / np.pi,
LR0_data["range"],
color=color_map_left(0.2),
marker='o',
ls='None',
label='Left RDR, beam 0')
number_of_dets_left_processed += np.size(LR0_data["mcc"])
if selection["beam_tp"].count(1):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [1]
LR1_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR1_data["rvelocity"],
vel_range,
color=color_map_left(0.4),
normed=1,
label='beam 1')
f1ax[0].plot(LR1_data["x"],
LR1_data["y"],
color=color_map_left(0.4),
marker='o',
ls='None',
label='Left RDR, beam 1')
f1ax[6].plot(-180 * LR1_data["razimuth"] / np.pi,
LR1_data["rvelocity"],
color=color_map_left(0.4),
marker='o',
ls='None',
label='Left RDR, beam 1')
f1ax[5].plot(-180 * LR1_data["razimuth"] / np.pi,
LR1_data["range"],
color=color_map_left(0.4),
marker='o',
ls='None',
label='Left RDR, beam 1')
number_of_dets_left_processed += np.size(LR1_data["mcc"])
if selection["beam_tp"].count(2):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [2]
LR2_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR2_data["velocity"],
vel_range,
color=color_map_left(0.6),
normed=1,
label='beam 2')
f1ax[0].plot(LR2_data["x"],
LR2_data["y"],
color=color_map_left(0.6),
marker='o',
ls='None',
label='Left RDR, beam 2')
f1ax[6].plot(-180 * LR2_data["azimuth"] / np.pi,
LR2_data["velocity"],
color=color_map_left(0.6),
marker='o',
ls='None',
label='Left RDR, beam 2')
f1ax[5].plot(-180 * LR2_data["azimuth"] / np.pi,
LR2_data["range"],
color=color_map_left(0.6),
marker='o',
ls='None',
label='Left RDR, beam 2')
number_of_dets_left_processed += np.size(LR2_data["mcc"])
if selection["beam_tp"].count(3):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [3]
LR3_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR3_data["rvelocity"],
vel_range,
color=color_map_left(0.8),
normed=1,
label='beam 3')
f1ax[0].plot(LR3_data["x"],
LR3_data["y"],
color=color_map_left(0.8),
marker='o',
ls='None',
label='Left RDR, beam 3')
f1ax[6].plot(-180 * LR3_data["razimuth"] / np.pi,
LR3_data["rvelocity"],
color=color_map_left(0.8),
marker='o',
ls='None',
label='Left RDR, beam 3')
f1ax[5].plot(-180 * LR3_data["razimuth"] / np.pi,
LR3_data["range"],
color=color_map_left(0.8),
marker='o',
ls='None',
label='Left RDR, beam 3')
number_of_dets_left_processed += np.size(LR3_data["mcc"])
plt.draw()
else:
LR_data_exists = False
# Right radar plot
if lst_det_right:
RR_data = lst_det_right.get_array_detections_selected(
mcc=selection['mcc_tp'])
if RR_data["mcc"].any():
RR_data_exists = True
f1ax[4].hist(RR_data["velocity"],
vel_range,
color=color_map_left(0.4),
normed=1)
number_of_dets_right = np.size(RR_data["mcc"])
if selection["beam_tp"].count(0):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [0]
RR0_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR0_data["velocity"],
vel_range,
color=color_map_right(0.2),
normed=1,
label='beam 0')
f1ax[0].plot(RR0_data["x"],
RR0_data["y"],
color=color_map_right(0.2),
marker='o',
ls='None',
label='Right RDR, beam 0')
f1ax[6].plot(-180 * RR0_data["azimuth"] / np.pi,
RR0_data["velocity"],
color=color_map_right(0.2),
marker='o',
ls='None',
label='Right RDR, beam 0')
f1ax[5].plot(-180 * RR0_data["azimuth"] / np.pi,
RR0_data["range"],
color=color_map_right(0.2),
marker='o',
ls='None',
label='Right RDR, beam 0')
number_of_dets_right_processed += np.size(RR0_data["mcc"])
if selection["beam_tp"].count(1):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [1]
RR1_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR1_data["velocity"],
vel_range,
color=color_map_right(0.4),
normed=1,
label='beam 1')
f1ax[0].plot(RR1_data["x"],
RR1_data["y"],
color=color_map_right(0.4),
marker='o',
ls='None',
label='Right RDR, beam 1')
f1ax[6].plot(-180 * RR1_data["azimuth"] / np.pi,
RR1_data["velocity"],
color=color_map_right(0.4),
marker='o',
ls='None',
label='Right RDR, beam 1')
f1ax[5].plot(-180 * RR1_data["azimuth"] / np.pi,
RR1_data["range"],
color=color_map_right(0.4),
marker='o',
ls='None',
label='Right RDR, beam 1')
number_of_dets_right_processed += np.size(RR1_data["mcc"])
if selection["beam_tp"].count(2):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [2]
RR2_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR2_data["velocity"],
vel_range,
color=color_map_right(0.6),
normed=1,
label='beam 2')
f1ax[0].plot(RR2_data["x"],
RR2_data["y"],
color=color_map_right(0.6),
marker='o',
ls='None',
label='Right RDR, beam 2')
f1ax[6].plot(-180 * RR2_data["azimuth"] / np.pi,
RR2_data["velocity"],
color=color_map_right(0.6),
marker='o',
ls='None',
label='Right RDR, beam 2')
f1ax[5].plot(-180 * RR2_data["azimuth"] / np.pi,
RR2_data["range"],
color=color_map_right(0.6),
marker='o',
ls='None',
label='Right RDR, beam 2')
number_of_dets_right_processed += np.size(RR2_data["mcc"])
if selection["beam_tp"].count(3):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [3]
RR3_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR3_data["velocity"],
vel_range,
color=color_map_right(0.8),
normed=1,
label='beam 3')
f1ax[0].plot(RR3_data["x"],
RR3_data["y"],
color=color_map_right(0.8),
marker='o',
ls='None',
label='Right RDR, beam 3')
f1ax[6].plot(-180 * RR3_data["azimuth"] / np.pi,
RR3_data["velocity"],
color=color_map_right(0.8),
marker='o',
ls='None',
label='Right RDR, beam 3')
f1ax[5].plot(-180 * RR3_data["azimuth"] / np.pi,
RR3_data["range"],
color=color_map_right(0.8),
marker='o',
ls='None',
label='Right RDR, beam 3')
number_of_dets_right_processed += np.size(RR3_data["mcc"])
plt.draw()
else:
RR_data_exists = False
if LR_data_exists and RR_data_exists:
lgd2 = f1ax[0].legend(loc='upper right', bbox_to_anchor=(1.0, 1.0))
f1ax[0].set_xlabel('x [meters]')
f1ax[0].set_ylabel('y [meters]')
f1ax[1].set_ylabel('Left RDR, separate')
f1ax[1].set_xlabel('Velocity $v(mcc)$ [m/s]')
f1ax[2].set_ylabel('Left RDR, all')
f1ax[3].set_ylabel('Right RDR, separate')
f1ax[4].set_ylabel('Right RDR, all')
f1ax[5].set_xlabel('Azimuth [deg]')
f1ax[5].set_ylabel('Range [meters]')
f1ax[6].set_ylabel('Velocity [km h$^{-1}$]')
tit = "MCC: %08d Num of Det: L=%d R=%d In Selected Beams: L=%d R=%d" % (
selection["mcc_tp"][0], number_of_dets_left, number_of_dets_right,
number_of_dets_left_processed, number_of_dets_right_processed)
f1.suptitle(tit, fontsize=14, fontweight='bold')
if fname_det:
f1.savefig(fname_det, format='eps', dpi=1200)
else:
plt.show()
else:
print("Nothing to plot for MCC:", selection["mcc_tp"])
