def plot_timeVfreq(date_operator, collection):
if collection == "drop":
field = "pickup_time"
elif collection == "pickup":
field = "drop_time"
else:
raise Exception(
"Error: the collection arguments to timeVfreq are 'drop', or 'pickup'. Not",
collection)
graph_size = {"hour": (0, 23), "dayOfYear": (1, 366), "dayOfWeek": (1, 8)}
if date_operator not in graph_size.keys():
raise Exception(
"Error: the date_operator argument to timeVfreq must be 'hour', 'dayOfWeek', or 'dayOfYear'. Not",
date_operator)
tvf = get_timeVfreq(date_operator, collection, field)
min_freq = ndarray.min(tvf[1])
max_freq = ndarray.max(tvf[1])
fig = pyplot.figure(1, figsize=(7, 6))
myplot = fig.add_subplot(111)
myplot.set_xlim(graph_size[date_operator])
norm = colors.Normalize(min_freq, max_freq)
for i in range(len(tvf[0])):
color = colors.rgb2hex(cm.cool(norm(tvf[1][i])))
pyplot.bar(tvf[0][i], tvf[1][i], color=color, ec=color, align='edge')
pyplot.xlabel(date_operator)
pyplot.ylabel("# of rides")
pyplot.savefig(date_operator + "_" + collection, dpi=180)
def plot_timeVfreq(date_operator, collection):
if collection == "drop":
field = "pickup_time"
elif collection == "pickup":
field = "drop_time"
else:
raise Exception("Error: the collection arguments to timeVfreq are 'drop', or 'pickup'. Not", collection)
graph_size = {"hour":(0, 23), "dayOfYear":(1,366), "dayOfWeek":(1,8)}
if date_operator not in graph_size.keys():
raise Exception("Error: the date_operator argument to timeVfreq must be 'hour', 'dayOfWeek', or 'dayOfYear'. Not", date_operator)
tvf = get_timeVfreq(date_operator, collection, field)
min_freq = ndarray.min(tvf[1])
max_freq = ndarray.max(tvf[1])
fig = pyplot.figure(1, figsize=(7, 6))
myplot = fig.add_subplot(111)
myplot.set_xlim(graph_size[date_operator])
norm = colors.Normalize(min_freq, max_freq)
for i in range(len(tvf[0])):
color = colors.rgb2hex(cm.cool(norm(tvf[1][i])))
pyplot.bar(tvf[0][i], tvf[1][i], color=color, ec=color, align='edge')
pyplot.xlabel(date_operator)
pyplot.ylabel("# of rides")
pyplot.savefig(date_operator + "_" + collection, dpi=180)
def min(self, *args, **kwargs):
"""
Returns the minimum Date.
For a description of the input parameters, please refer to numpy.min.
"""
obj = ndarray.min(self, *args, **kwargs)
if not obj.shape:
return Date(self.freq, obj)
return obj
def min(self, *args, **kwargs):
"""
Returns the minimum Date.
For a description of the input parameters, please refer to numpy.min.
"""
obj = ndarray.min(self, *args, **kwargs)
if not obj.shape:
return Date(self.freq, obj)
return obj
def BackgroundImage(request):
chad = User.objects.get(id=1)
image = ThreeDimensional.objects.filter(user=chad)[0]
image_handle = nibabel.load(image.brain_image.file.name)
image_data = image_handle.get_data()
image_list_data = image_data.tolist()
#for some reason ndarray.max returns an ndarray with 1 member
#which is a numpy.flaot32. this converts all that to a regular python float
max = ndarray.max(image_data).tolist()
min = ndarray.min(image_data).tolist()
json_object = {
'data' : image_list_data,
'max' : max,
'min' : min,
}
json_data = json.dumps(json_object)
return HttpResponse(json_data, mimetype='application/json')
def main_test():
# As a main function, this function tests other functions written in domain_functions.py:
# - compute triangulation of domain
# -
nodes = np.array([[0., 0.], [0., 10.], [15., 10.], [15., 0.], [6., 10.],
[9., 10.], [11., 0.], [2., 8.], [8., 7.], [11., 2.],
[5., 5.]])
tri = spatial.Delaunay(nodes)
print ''
print 'Entered points, triangles by vertex, and neighbors:'
print tri.points # Numbering of the points is in order of input
print ''
print tri.simplices # From point numbers, give the triangles, starting with # 0
print ''
print tri.neighbors # Give the triangle number of neighbors, and -1 for boundary
# Test which triangle:
# print tri.find_simplex(np.array([2., 2.]))
# Choose a simplex and facet control idx:
num = 0
facet_id = 0
# Extract points:
points = tri.points
vertices = tri.simplices
vertices_here = vertices[num, :]
print ''
print 'vertices_here:'
print vertices_here
# Extract normals:
nhat_array = nhat_tri_facet(tri, num)
print ''
print 'Local normal vectors:'
print nhat_array
u_optimal, fg_array = u_for_F_v1(tri, num, 0.01, np.eye(2), facet_id)
u_stay, fg_array_stay = u_for_stay_v1(tri, num, 0.01, np.eye(2))
print ''
print 'Optimal u at each vertex:'
print u_optimal
print ''
print 'fg_array for select triangle and facet:'
print fg_array
print ''
print 'Optimal u to stay:'
print u_stay
fg_master_array = fg_for_domain(tri, 0.01, np.eye(2), 2)
print ''
print 'fg_master_array:'
print fg_master_array
x_array = points[vertices_here, 0]
y_array = points[vertices_here, 1]
x_lim = np.array([ndarray.min(x_array), ndarray.max(x_array)])
y_lim = np.array([ndarray.min(y_array), ndarray.max(y_array)])
domain_plot = 1
control_plot = 1
# Plot partitioning of domain:
if domain_plot == 1:
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.triplot(nodes[:, 0], nodes[:, 1], tri.simplices.copy())
plt.plot(nodes[:, 0], nodes[:, 1], 'o')
plt.title('Partitioned Domain')
plt.grid(linestyle="--", linewidth=0.1, color='.25', zorder=-10)
# Quiver velocity field to leave selected triangle:
if control_plot == 1:
X, Y = np.meshgrid(np.arange(x_lim[0] - 0.5, x_lim[1] + 0.5, 0.5),
np.arange(y_lim[0] - 0.5, y_lim[1] + 0.5, 0.5))
U = fg_array[0, 0] * X + fg_array[0, 1] * Y + fg_array[0, 2]
V = fg_array[1, 0] * X + fg_array[1, 1] * Y + fg_array[1, 2]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.title(
'Control Vector Field for Chosen Simplex - Exit Chosen Facet')
ax.triplot(nodes[:, 0], nodes[:, 1], tri.simplices.copy())
ax.plot(nodes[:, 0], nodes[:, 1], 'o')
ax.quiver(X, Y, U, V)
ax.grid(linestyle="--", linewidth=0.1, color='.25', zorder=-10)
# ax.set_xlim(x_lim)
# ax.set_ylim(y_lim)
X, Y = np.meshgrid(np.arange(x_lim[0] - 0.5, x_lim[1] + 0.5, 0.5),
np.arange(y_lim[0] - 0.5, y_lim[1] + 0.5, 0.5))
U = fg_array_stay[0, 0] * X + fg_array_stay[0, 1] * Y + fg_array_stay[
0, 2]
V = fg_array_stay[1, 0] * X + fg_array_stay[1, 1] * Y + fg_array_stay[
1, 2]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.title('Control Vector Field for Chosen Simplex - Remain')
ax.triplot(nodes[:, 0], nodes[:, 1], tri.simplices.copy())
ax.plot(nodes[:, 0], nodes[:, 1], 'o')
ax.quiver(X, Y, U, V)
ax.grid(linestyle="--", linewidth=0.1, color='.25', zorder=-10)
# ax.set_xlim(x_lim)
# ax.set_ylim(y_lim)
if (domain_plot == 1) or (control_plot == 1):
plt.show()
return
# Execute test:
# main_test()
