def clean_points(self):
points = []
if not self.cleaned_data.get('points'):
return points
try:
json_points = json.loads(self.cleaned_data.get('points'))
if not json_points:
return points
for p in json_points:
_id = p.get('properties').get('id')
if _id:
point = models.Point.objects.get(id=_id)
else:
point = models.Point()
point.coords = Point(p.get('coordinates'))
point.address = p.get('properties').get('address')
points.append(point)
return points
except ValueError:
pass
return points
def test_basic_u(self):
np.random.seed(401)
z_ref = -1
width = 10
length = 10
# planes
x_planes = []
x_offsets = np.cumsum(np.random.randint(1, 3, 6)) - width / 2
for x in x_offsets:
x_planes.append(models.Plane.from_axis_distance(axis=np.array([1, 0, 0]), distance=x))
y_planes = []
y_offsets = np.cumsum(np.random.randint(1, 3, 5)) - length / 2
for y in y_offsets:
y_planes.append(models.Plane.from_axis_distance(axis=np.array([0, 1, 0]), distance=y))
# boundaries
boundaries = [simulator.rectangle(x_planes[4], x=np.mean([y_offsets[3], y_offsets[2]]), y=0, w=y_offsets[3]-y_offsets[2]-0.3, h=2),
simulator.rectangle(x_planes[5], x=np.mean([y_offsets[4], y_offsets[1]]), y=0, w=y_offsets[4]-y_offsets[1]-0.5, h=2),
simulator.rectangle(y_planes[1], x=-np.mean([x_offsets[5], x_offsets[1]]), y=0, w=x_offsets[5]-x_offsets[1]-0.4, h=2),
simulator.rectangle(y_planes[2], x=-np.mean([x_offsets[4], x_offsets[1]]), y=0, w=x_offsets[4]-x_offsets[1]-0.2, h=2),
simulator.rectangle(y_planes[3], x=-np.mean([x_offsets[4], x_offsets[1]]), y=0, w=x_offsets[4]-x_offsets[1]-0.25, h=2),
simulator.rectangle(y_planes[4], x=-np.mean([x_offsets[5], x_offsets[1]]), y=0, w=x_offsets[5]-x_offsets[1]-0.1, h=2)
]
# evidence
evidence_index = [((2, 2), (1, 1)),
((4, 2), (2, 1)),
((5, 2), (4, 1)),
((5, 4), (4, 2)),
((4, 4), (3, 3)),
((3, 4), (2, 3)),
((2, 4), (1, 3)),
((3, 1), (2, 0))]
evidence = [models.Point(np.array([-2, 3, 0]))]
for ev in evidence_index:
tr_corner = np.array([x_offsets[ev[0][0]], y_offsets[ev[0][1]]])
bl_corner = np.array([x_offsets[ev[1][0]], y_offsets[ev[1][1]]])
diff = tr_corner - bl_corner
center = np.mean(np.array([tr_corner, bl_corner]), axis=0)
ellipse = simulator.ellipse(float(diff[0]) / 2, float(diff[1]) / 2, 3).rigid(np.eye(2), center)
evidence.append(ellipse)
# construct
cell_complex = models.CellComplex2D(z_ref=z_ref, width=width, length=length, evidence=evidence)
# cell_complex = models.CellComplex2D(z_ref=z_ref, width=width, length=length)
for p in x_planes + y_planes:
cell_complex.insert_partition(p)
for b in boundaries:
cell_complex.insert_boundary(b)
speculator = estimators.FloorPlanSpeculator(cell_complex, horizon=1)
scene_graph = speculator.floorplan()
# scene_graph = cell_complex.cell_graph()
cell_complex.draw(scene_graph)
def insert_Points_into_DB():
point_1 = models.Point(
score=5,
question=
'Из какого мультфильма строчки песни: Взгляни вокруг, оглянись назад, духи с тобой связаться хотят',
wrong_answer='Скуби-Ду;Гравити Фолз;Рик и Морти',
right_answer='Шаман Кинг',
reaction=
'Неверно, это начало опенинга к аниме Шаман Кинг;Верно, а у тебя хорошее детство было :Р'
)
point_2 = models.Point(
score=5,
question=
'Из какого фильма строчка песни: Со мною вот что происходит, ко мне мой лучший друг не ходит',
wrong_answer='Двенадцать стульев;Полосатый рейс;Кавказская пленница',
right_answer='Ирония судьбы',
reaction=
'Неверно, это начальная песня советского фильма Ирония Судьбы;Верно!')
point_3 = models.Point(
score=10,
question=
'Из какого мультфильма строчка песни: But the meteor men beg to differ',
wrong_answer='Холодное сердце;Ледниковый период;Би муви',
right_answer='Шрек',
reaction=
'Неверно, это же All stars из Шрека;Верно, да ты фанат Шрека!!!')
point_4 = models.Point(
score=10,
question=
'Из какого фильма строчка песни: And the last known survivor stalks his prey in the night',
wrong_answer='Телохранитель;Бойцовский клуб;Терминатор',
right_answer='Рокки',
reaction=
'Неверно, это Eye of the tiger, знаменитая песня из Рокки;Верно!')
point_1.save()
point_2.save()
point_3.save()
point_4.save()
def save_points(self):
geo_json = self.cleaned_data.get('geo_json')
points = []
for p in geo_json.get('points', []):
point = models.Point()
point.helprequest = self.instance
point.user = self.instance.requester
point.coords = Point(p.get('coordinates'))
point.address = p.get('properties').get('address')
points.append(point)
self.instance.point_set.bulk_create(points)
return points
plt.show()
def iterateClusters(vectors, clusters, old=[]):
new = assignCluster(vectors, clusters)
iterateClusters(vectors, new, clusters)
if __name__ == "__main__":
vectors = []
clusters = []
k = 7 # Number of clusters
data = helpers.readData('data_1024.csv', '\t')
for coord in zip(data['Distance_Feature'], data['Speeding_Feature']):
vector = models.Point(coord, None)
vectors.append(vector)
clusters = generateClusters(vectors, k)
coordList = [
] # Used for detecting duplicates, once a duplicate is shown the loop will break.
while True:
clusters = assignCluster(vectors, clusters)
for y in clusters:
coordList.append(y.coordinates)
print(str(y.coordinates) + '\n -------')
if len(helpers.listDuplicates(coordList)) != 0:
def get(self):
template = JINJA_ENVIRONMENT.get_template('game.html')
team_one_name = self.request.get('one')
team_two_name = self.request.get('two')
hypeTable = models.HypeTable.query(
ndb.OR(models.HypeTable.teamOneName == team_one_name,
models.HypeTable.teamOneName == team_two_name)).fetch()[0]
team_one_coordinates = models.GeoData.query(
models.GeoData.teamName ==
hypeTable.teamOneName).fetch()[0].coordinates.split('|')
team_two_coordinates = models.GeoData.query(
models.GeoData.teamName ==
hypeTable.teamTwoName).fetch()[0].coordinates.split('|')
team_one_points = [
models.Point(point) for point in team_one_coordinates
]
team_two_points = [
models.Point(point) for point in team_two_coordinates
]
team_one_top_list = models.TopTweet.query(
team_one_name == models.TopTweet.teamName).fetch()
team_two_top_list = models.TopTweet.query(
team_two_name == models.TopTweet.teamName).fetch()
team_one_top = team_one_top_list[0] if len(
team_one_top_list) > 0 else {}
team_two_top = team_two_top_list[0] if len(
team_two_top_list) > 0 else {}
latest_tweets = models.LatestTweets.query(
ndb.OR(models.LatestTweets.teamName == team_one_name,
models.LatestTweets.teamName == team_two_name)).fetch()
random.shuffle(latest_tweets)
template_values = {
'game_title': hypeTable.gameTitle,
'game_time': hypeTable.gameTime,
'game_location': hypeTable.gameLocation,
'team_one_color': hypeTable.teamOneColor,
'team_two_color': hypeTable.teamTwoColor,
'team_one_name': hypeTable.teamOneName,
'team_two_name': hypeTable.teamTwoName,
'team_one_total': hypeTable.teamOneTweetTotal,
'team_two_total': hypeTable.teamTwoTweetTotal,
'team_one_hype': hypeTable.teamOneHype,
'team_two_hype': hypeTable.teamTwoHype,
'team_one_image': hypeTable.teamOneImage,
'team_two_image': hypeTable.teamTwoImage,
'team_one_hashtags': hypeTable.teamOneHashTags.split(','),
'team_two_hashtags': hypeTable.teamTwoHashTags.split(','),
'team_one_tweets': team_one_points,
'team_two_tweets': team_two_points,
'team_one_top': team_one_top,
'team_two_top': team_two_top,
'latest_tweets': latest_tweets,
'hype_history': hypeTable.gameHypeHistory,
'time_history': hypeTable.gameTimeHistory
}
self.response.write(template.render(template_values))
def print(*args, **kwargs):
""" custom print function, adding functionality for Points """
new_args = []
for arg in args:
if builtins.isinstance(arg, models.Point):
new_args.append("({0}, {1})".format(arg.x, arg.y))
else:
new_args.append(arg)
builtins.print(*new_args, **kwargs)
p = 2**256 - 2**32 - 2**9 - 2**8 - 2**7 - 2**6 - 2**4 - 1
curve = models.Curve(a=0, b=7, p=p)
G_x = int("79be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798",
16)
G_y = int("483ada7726a3c4655da4fbfc0e1108a8fd17b448a68554199c47d08ffb10d4b8",
16)
G = models.Point(G_x, G_y, curve)
priv_key0 = 17 # should pick a huge number for priv_key
pub_key0 = G * priv_key0
priv_key1 = 23
pub_key1 = G * priv_key1
print(pub_key0, pub_key1)
print(pub_key0 * priv_key1 == pub_key1 * priv_key0) # should print True