def task():
fig = mtp.new_figure((6,4))
ax = fig.add_subplot(1,1,1)
x = [0, self.model.days, self.model.days, self.model.days*1.1]
y = [1,1,0,0]
ax.plot(x, y)
ax.set(xlabel=_text["days"], ylabel=_text["int"], xlim=[0, self.model.days*1.1], ylim=[-0.1,1.1])
return fig
def task():
fig = mtp.new_figure((6,4))
ax = fig.add_subplot(1,1,1)
x = np.linspace(0, self.model.scale*5, 100)
kernel = self.model.make_kernel()
ax.plot(x, kernel(x))
ax.set(xlabel=_text["days"], ylabel=_text["int"], xlim=[0, self.model.scale*5], ylim=[0,1])
return fig
def task():
fig = mtp.new_figure((6,6))
ax = fig.add_subplot(1,1,1)
xcs, ycs = coords
self.sample_kernel(kernel_provider, [xcs, ycs], ax)
ax.set_aspect(1)
fig.set_tight_layout("tight")
return fig
def _plot_weight(weight, bandwidth):
fig = mtp.new_figure((5, 5))
ax = fig.add_subplot(1, 1, 1)
xc = np.linspace(-bandwidth, bandwidth, 50)
yc = weight(xc, [0] * len(xc))
ax.plot(xc, yc, color="black")
ax.set(xlabel="Distance in metres")
ax.set(ylabel="Relative risk")
fig.set_tight_layout(True)
return fig
def make_fig():
fig = mtp.new_figure((20, 20))
ax = fig.add_subplot(1, 1, 1)
prediction = self.model.current_prediction
if level == -1:
plot_risk(prediction, ax, adjust_task)
else:
plot_coverage(prediction, level, ax, adjust_task)
ax.set_aspect(1)
fig.set_tight_layout(True)
return fig
def task():
fig = mtp.new_figure(size=(8, 8))
ax = fig.add_subplot(1, 1, 1)
self._try_add_patch(ax, geo)
ax.scatter(coords[0],
coords[1],
marker="+",
color="black",
alpha=0.5)
ax.set_aspect(1)
fig.set_tight_layout("tight")
return fig
def task():
xmin, ymin, xmax, ymax = geo.bounds
xdelta = (xmax - xmin) / 100 * 2
ydelta = (ymax - ymin) / 100 * 2
fig = mtp.new_figure(size=(8, 8))
ax = fig.add_subplot(1, 1, 1)
self._try_add_patch(ax, geo)
ax.set(xlim=[xmin - xdelta, xmax + xdelta],
ylim=[ymin - ydelta, ymax + ydelta])
ax.set_aspect(1)
fig.set_tight_layout("tight")
return fig
def _plot_task(self):
fig = mtp.new_figure((5,5))
ax = fig.add_subplot(1,1,1)
dist_obj = self._model.grid_distance()
xcs = np.array([-3, -2, -1, 0, 1, 2, 3, 4])
ycs = np.array([-3, -2, -1, 0, 1, 2, 3, 4])
dists = np.empty((len(ycs)-1,len(xcs)-1))
for ix, x in enumerate(xcs[:-1]):
for iy, y in enumerate(ycs[:-1]):
dists[iy][ix] = dist_obj(0,0,x,y)
pcm = ax.pcolormesh(xcs, ycs, dists, cmap="rainbow")
fig.colorbar(pcm)
ax.set(xlabel=_text["dgx"], ylabel=_text["dgy"])
fig.set_tight_layout(True)
return fig
def _plot_task(self):
fig = mtp.new_figure((5,5))
ax = fig.add_subplot(1,1,1)
weight = self._model.weight()
xcs = np.arange(0, self._model.space_bandwidth + 1)
ycs = np.arange(0, self._model.time_bandwidth + 1)
dists = np.empty((len(ycs)-1,len(xcs)-1))
for ix, d_space in enumerate(xcs[:-1]):
for iy, d_time in enumerate(ycs[:-1]):
dists[iy][ix] = weight(d_time,d_space)
num, unit = ProHotspotView.timeunit(self._parent_model.time_window_length)
pcm = ax.pcolormesh(xcs, ycs * num, dists, cmap="rainbow")
fig.colorbar(pcm)
yl = _text["time_bin_choices"][unit]
ax.set(xlabel=_text["kgx"], ylabel=yl)
fig.set_tight_layout(True)
return fig
def _plot_task(self):
fig = mtp.new_figure((5, 5))
ax = fig.add_subplot(1, 1, 1)
weight = self._model.weight()
xcs = np.linspace(0, self._model.space_bandwidth, 100)
ycs = np.linspace(0, self._model.time_bandwidth, 100)
dists = np.empty((len(ycs) - 1, len(xcs) - 1))
for ix, d_space in enumerate(xcs[:-1]):
for iy, d_time in enumerate(ycs[:-1]):
dists[iy][ix] = weight(d_time, d_space)
xscale = self._parent_model.space_length
yscale = self._parent_model.time_window_length / datetime.timedelta(
hours=1)
pcm = ax.pcolormesh(xcs * xscale, ycs * yscale, dists, cmap="rainbow")
fig.colorbar(pcm)
ax.set(xlabel=_text["kgx"], ylabel=_text["kgy"])
fig.set_tight_layout(True)
return fig
def _plot_task(self):
fig = mtp.new_figure((5, 5))
ax = fig.add_subplot(1, 1, 1)
dist_obj = self._model.grid_distance()
bandwidth = self._model.model.space_length
xcs = np.linspace(-5, 5, 100)
ycs = np.linspace(-5, 5, 100)
dists = np.empty((len(ycs) - 1, len(xcs) - 1))
for ix, x in enumerate(xcs[:-1]):
for iy, y in enumerate(ycs[:-1]):
dists[iy][ix] = dist_obj(0, 0, x, y)
pcm = ax.pcolormesh(xcs * bandwidth,
ycs * bandwidth,
dists,
cmap="rainbow")
fig.colorbar(pcm)
ax.set(xlabel=_text["dgx"], ylabel=_text["dgy"])
fig.set_tight_layout(True)
return fig
def make_fig():
xmin, xmax = _np.min(coords[0]), _np.max(coords[0])
xd = (xmax - xmin) / 100 * 3
xmin, xmax = xmin - xd, xmax + xd
ymin, ymax = _np.min(coords[1]), _np.max(coords[1])
yd = (ymax - ymin) / 100 * 3
ymin, ymax = ymin - yd, ymax + yd
width = xmax - xmin
height = ymax - ymin
if width == 0 or height == 0:
size = (10, 10)
else:
height = height / width * 10.0
width = 10.0
if height > 10.0:
width = 100.0 / height
height = 10.0
size = (width, height)
fig = mtp.new_figure(size)
ax = fig.add_subplot(1, 1, 1)
ax.scatter(coords[0],
coords[1],
marker="x",
color="black",
alpha=0.5)
lc = open_cp.plot.lines_from_regular_grid(
self._controller.get_grid())
ax.add_collection(
mtp.matplotlib.collections.LineCollection(lc,
color="black",
linewidth=0.5))
ax.set(xlim=[xmin, xmax], ylim=[ymin, ymax])
ax.set_aspect(1.0)
fig.set_tight_layout(True)
return fig