def stage_0_read(reactor_id):
children = None
if reactor_id==None:
children = database_manager.fetch_children()
elif isinstance(reactor_id,list):
children = database_manager.fetch_children_subset(id=reactor_id)
hanashi.create_new_batch(np.array([[None] for i in range(len(reactor_id))]),servers=reactor_id)
hanashi.step()
def uniform(alpha):
with open("../data/spectra/neutral_spectrum_components.json") as f:
coefficients = json.load(f)["coefficients"]
coefficients = np.array(coefficients)
coefficients = alpha * coefficients
hanashi.create_new_batch(
coefficients.reshape((1, coefficients.shape[0])))
hanashi.step()
def assignments():
if request.method == "GET":
database_manager.get_exsiting_batch()
assignments = database_manager.fetch_assignments()
return render_template('pages/assignments.html',
assignments=assignments)
elif request.method == "POST":
"""
Manually add new data.
"""
data = request.json["matrix"] #list of lists
hanashi.create_new_batch(np.array(data))
return json.dumps({'success': True}), 200, {
'ContentType': 'application/json'
}
def fitness(X_cube, mode="volume", high=None, low=None, E=1):
"""
mode: str
-volume: expects the energy constranint to be smaller than a threshold
-surface: expects the energy constraint to be equal to a threshold
"""
high = np.ones(X_cube.shape[1]) if not high else high
low = np.zeros(X_cube.shape[1]) if not low else low
scale = np.array([high, low])
if mode == "volume":
X = hypercube_to_simplex(X_cube.copy(), scale)
elif mode == "surface":
#In this case there is a dimension reduction
X = hypercube_to_simplex(X_cube.copy(), scale)
u = E - X.sum(axis=1)
u = u.reshape((u.shape[0], 1))
X = np.hstack([u, X])
print(X)
shared_memory.set('batch_done', False)
batch = hanashi.create_new_batch(X)
R = hanashi.step()
wait(lambda: shared_memory.get('batch_done'))
id = batch["id"]
Y = hanashi.get_from_id(id, return_request_id=True)
Y = {u[1]: u[0] for u in Y}
y = [Y[k] for k in np.array(R)[:, 2].astype(int)]
return np.array(y).ravel()
def set_color(self):
assert len(self.colors) != 0 and isinstance(self.colors,list)
color_partitions = np.array_split(np.array(self._colors_index),len(self.reactors))
color_partition_dict = dict(zip(self.reactors,color_partitions))
X = np.repeat([self.color_base],len(self.colors),acis=0)
X += np.eye(X.shape[0],X.shape[1],0)*self.color_current
R = hanashi.create_new_batch(X,self.reactors)
self.color_batch_id = R["batch_id"]