def new_update_view(*args, **kwargs):
global x0, x1
_update_view_orig(*args, **kwargs)
s = 'toolbar_event'
event = Event(s, canvas)
canvas.callbacks.process(s, Event('toolbar_event', canvas))
x0, x1 = mainplot.get_xlim()
def new_release_pan(*args, **kwargs):
global x0, x1
release_pan_orig(*args, **kwargs)
s = 'toolbar_event'
event = Event(s, canvas)
canvas.callbacks.process(s, Event('toolbar_event', canvas))
x0, x1 = mainplot.get_xlim()
def set_history_buttons(self):
can_backward = self._nav_stack._pos > 0
can_forward = self._nav_stack._pos < len(self._nav_stack._elements) - 1
self._actions['back'].setEnabled(can_backward)
self._actions['forward'].setEnabled(can_forward)
event = Event('history', self)
self.canvas.callbacks.process('history', event)
def systemOnclick():
event = Event("mockEvent",system.fig.canvas)
event.xdata = float(request.args['startX'])
event.ydata = float(request.args['startY'])
system.on_button(event)
event.xdata = float(request.args['endX'])
event.ydata = float(request.args['endY'])
event.button = int(request.args['type'])
system.off_button(event)
return ""
def networkOnclick():
event = Event("mockEvent",network.fig.canvas);
event.xdata = float(request.args['startX'])
event.ydata = float(request.args['startY'])
event.button = int(request.args['type'])
network.on_button(event)
event.xdata = float(request.args['endX'])
event.ydata = float(request.args['endY'])
event.button = int(request.args['type'])
network.off_button(event)
return ""
def test_query_grid_on_button_press():
rmg = RasterModelGrid((5, 5))
imshow_grid(rmg, rmg.nodes, cmap='RdYlBu')
# Programmatically create an event near the grid center.
event = Event('simulated_event', gcf().canvas)
event.xdata = int(rmg.number_of_node_columns * 0.5)
event.ydata = int(rmg.number_of_node_rows * 0.5)
results = query_grid_on_button_press(event, rmg)
x_coord = results['grid location']['x_coord']
y_coord = results['grid location']['x_coord']
msg = 'Items: Simulated matplotlib event and query results.'
assert_equal(x_coord, event.xdata, msg)
assert_equal(y_coord, event.ydata, msg)
msg = 'Items: Node ID and grid coordinates of simulated matplotlib event.'
node = rmg.grid_coords_to_node_id(event.xdata, event.ydata)
assert_equal(results['node']['ID'], node, msg)
def test_query_grid_on_button_press():
rmg = RasterModelGrid((5, 5))
imshow_grid(rmg, rmg.nodes, cmap="RdYlBu")
# Programmatically create an event near the grid center.
event = Event("simulated_event", gcf().canvas)
event.xdata = int(rmg.number_of_node_columns * 0.5)
event.ydata = int(rmg.number_of_node_rows * 0.5)
results = query_grid_on_button_press(event, rmg)
x_coord = results["grid location"]["x_coord"]
y_coord = results["grid location"]["x_coord"]
msg = "Items: Simulated matplotlib event and query results."
assert_equal(x_coord, event.xdata, msg)
assert_equal(y_coord, event.ydata, msg)
msg = "Items: Node ID and grid coordinates of simulated matplotlib event."
node = rmg.grid_coords_to_node_id(event.xdata, event.ydata)
assert_equal(results["node"]["ID"], node, msg)
def new_release_zoom(self, *args, **kwargs):
s = 'release_zoom_event'
event = Event(s, self)
event.foo = 3
self.canvas.callbacks.process(s, event)
release_zoom(self, *args, **kwargs)
def new_press_zoom(self, *args, **kwargs):
s = 'press_zoom_event'
event = Event(s, self)
event.foo = 2
self.canvas.callbacks.process(s, event)
press_zoom(self, *args, **kwargs)
def new_home(self, *args, **kwargs):
s = 'home_event'
event = Event(s, self)
event.foo = 0
self.canvas.callbacks.process(s, event)
home(self, *args, **kwargs)
def leave_notify_event(self, guiEvent=None):
event = Event('figure_leave_event', self, guiEvent)
self.callbacks.process('figure_leave_event', event)
def enter_notify_event(self, guiEvent=None, xy=None):
event = Event('figure_enter_event', self, guiEvent)
self.callbacks.process('figure_enter_event', event)
def new_release_pan(self, *args, **kwargs):
self.release_pan_orig(*args, **kwargs)
s = 'toolbar_event'
event = Event(s, self)
self.canvas.callbacks.process(s, Event('toolbar_event', self))
def new_backward(self, *args, **kwargs):
s = 'backward_event'
event = Event(s, self)
event.foo = 100
self.canvas.callbacks.process(s, event)
def new_forward(self, *args, **kwargs):
s = 'forward_event'
event = Event(s, self)
event.foo = 3
self.canvas.callbacks.process(s, event)
forward(self, *args, **kwargs)
def new_func(self, *args, **kwargs):
toolbar_function(self, *args, **kwargs)
event = Event(event_name, self)
self.canvas.callbacks.process(event_name, event)
def new_update_view(self, *args, **kwargs):
self._update_view_orig(*args, **kwargs)
s = 'toolbar_event'
event = Event(s, self)
self.canvas.callbacks.process(s, Event('toolbar_event', self))
def eval_task_reconstruct(model: FilterbankModule, num_to_show: int):
model.eval()
model.to(device)
train_data, eval_data, dataset = util.load_datasets(device)
# plt.figure(figsize=(10, 8))
height = round(math.ceil(math.sqrt(num_to_show) * 0.7))
width = num_to_show // height
actual_num = width * height
# rand = random.randint(0, min(dataset.max_samples - actual_num, 1000))
fig, axs = plt.subplots(nrows=height * 2, ncols=width)
# register this callback to scroll pages of results by clicking on far left or far right of plot
# matplotlib should really let you hijack those < > buttons instead, but they don't generate events
def onclick(event: Event):
global viz_data_index
if event.guiEvent is not None:
for i in range(height):
for j in range(width):
axs[2 * i, j].clear()
axs[2 * i + 1, j].clear()
if event.guiEvent.x < 200:
viz_data_index = max(0, viz_data_index - actual_num)
elif event.guiEvent.x > event.canvas.figure.get_figwidth() - 200:
viz_data_index = min(dataset.max_samples - 1,
viz_data_index + actual_num)
samples = torch.Tensor(actual_num, model.window_size)
for i in range(actual_num):
if i < actual_num // 2:
samples[i] = eval_data[i + viz_data_index]
else:
samples[i] = train_data[i + viz_data_index]
# make sure this tensor is on the same device as the module
samples = samples.to(device)
# add 'channel' dim
padded_samples = samples.unsqueeze(1)
recons, lincombs = model.forward(padded_samples)
for i in range(height):
for j in range(width):
index = i * width + j
# squeeze to pop off the batch dim
sample_np = samples[index].cpu().detach().numpy()
combin_np = lincombs[index].cpu().detach().numpy()
recon_np = recons[index].cpu().detach().numpy()
axis_win = [x for x in range(model.window_size)]
axis_lincomb = [x for x in range(model.wavelet_kernels)]
axs[i * 2, j].plot(axis_win, recon_np)
axs[i * 2, j].plot(axis_win, sample_np)
axs[i * 2 + 1, j].plot(axis_lincomb, combin_np)
event.canvas.draw()
onclick(Event('fake-click', fig.canvas))
fig.canvas.mpl_connect('button_press_event', onclick)
plt.subplots_adjust(wspace=0.1, hspace=0)
plt.show()
