def test_all_samples_in_sample_server():
screen = CM.Screen()
print("Testing all samples in the sample server")
f = open("/root/Documents/paths.txt", "r")
paths = f.readlines()
print(paths)
PromptUtils(screen).enter_to_continue()
def sync(prompt=True):
clearDir('../data/s3')
run([
"aws", "s3", "sync", "s3://atlascampi", "../data/s3",
"--only-show-errors"
])
if prompt:
PromptUtils(screen).enter_to_continue()
def minimal_addition_minimum_flow(file):
try:
result = algo.minimal_addition_minimum_flow(file)
for index, res in enumerate(result):
table_print("D{}".format(index + 1), res)
except Exception as e:
Screen().println("\nError: %s\n" % e)
finally:
PromptUtils(Screen()).enter_to_continue()
def action(color_name):
text = """
Dark spruce forest frowned on either side of the frozen waterway.
The trees had been stripped by a recent wind of their white covering of frost,
and they seemed to lean toward each other, black and ominous, in the fading
light. A vast silence reigned over the land.
"""
print(color(text, fg=color_name))
PromptUtils(Screen()).enter_to_continue()
def floyd(file):
try:
result = algo.floyd(file)
for key, value in result.items():
table_print(key, value)
# print(fl.floyd(file))
except Exception as e:
Screen().println("\nError: %s\n" % e)
finally:
PromptUtils(Screen()).enter_to_continue()
def test_sample_on_network(sample):
# Find a random IP to execute the sample on
# TODO: Fetch the ips automatically from the connected list
x = randrange(3)
# ip = config.machineIPs[x]
# ip = '10.0.0.3'
ip = '127.0.0.1'
print("Random IP: " + ip)
sm.sampleAtOrigin(ip, sample, incubation_time)
screen = CM.Screen()
PromptUtils(screen).enter_to_continue()
def best_neighbour(file):
start = Screen().input(prompt="Zadaj startovaci vrchol: ")
start_index = string.ascii_uppercase.index(start.upper())
result = algo.best_neighbour(file, start_index)
table_print("", result[1])
path = [string.ascii_uppercase[node] for node, weight in result[0]]
weights = [weight for nodes, weight in result[0]]
print("->".join(path) + "->{}".format(start.upper()))
print(" + ".join(map(str, weights)) + "= {}".format(sum(weights)))
PromptUtils(Screen()).enter_to_continue()
def prim(file):
result = algo.prim(file)
edges = []
weights = []
for edge, weight in result:
v1 = string.ascii_uppercase[edge[0]]
v2 = string.ascii_uppercase[edge[1]]
edge = v1 + "->" + v2
edges.append(edge)
weights.append(weight)
print(" , ".join(edges))
print(" + ".join(map(str, weights)) + " = {}".format(sum(weights)))
PromptUtils(Screen()).enter_to_continue()
def cpm_table(file):
data = cpm.copm_cpm(algo.get_tasks(file))
table = BeautifulTable()
table.set_style(BeautifulTable.STYLE_BOX)
# create table headers
col_headers = ["Cinnost", "ZM", "KM", "ZP", "KP", "RC", "Kriticka"]
# Set column headers
table.column_headers = col_headers
for row in data:
table.append_row(row)
print(table)
PromptUtils(Screen()).enter_to_continue()
def pert_table(file):
data, standard_deviation = algo.pert(file)
table = BeautifulTable()
table.set_style(BeautifulTable.STYLE_BOX)
# create table headers
col_headers = [
"Cinnost", "Zavislosti", "aij", "mij", "bij", "trvanie", "odchylka",
"rozptyl"
]
# Set column headers
table.column_headers = col_headers
for row in data:
table.append_row(row)
print(table)
print("Smerodajna odchylka trvanie projektu q(Te)= {}".format(
standard_deviation))
PromptUtils(Screen()).enter_to_continue()
def create_graph(file):
dist_matrix = algo.get_matrix(file)
dist_matrix = np.where(dist_matrix == "M", 0, dist_matrix).astype(np.int)
graph = nx.Graph()
letters = string.ascii_uppercase
nodes = [letters[x] for x in range(dist_matrix.shape[0])]
graph.add_nodes_from(nodes)
edges = []
for x in range(dist_matrix.shape[0]):
for y in range(dist_matrix.shape[1]):
if dist_matrix[x][y] != 0:
edges.append(
[letters[x], letters[y], {
'weight': dist_matrix[x][y]
}])
graph.add_edges_from(edges)
# planar layout works well so far
# pos = nx.spring_layout(graph, seed=42)
pos = nx.planar_layout(graph)
nx.draw(graph, pos)
labels = nx.get_edge_attributes(graph, 'weight')
nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)
# nx.draw_networkx_labels(graph, pos, font_size=20, font_family="sans-serif")
nx.draw_networkx_labels(graph, pos)
graph_path = pathlib.Path(folder, "graph.png")
fig = plt.savefig(graph_path)
plt.show()
plt.close(fig)
# open file with default image program
# os.startfile(graph_path)
PromptUtils(Screen()).enter_to_continue()
def best_neighbour_matrix(file):
result = algo.best_neighbour_matrix(file)
table_print("E", result)
PromptUtils(Screen()).enter_to_continue()
def minimal_addition(file):
result = algo.minimal_addition(file)
for index, res in enumerate(result):
table_print("D{}".format(index + 1), res)
PromptUtils(Screen()).enter_to_continue()
def cleanLocal():
os.system("rm -rf ../data/s3/*")
PromptUtils(screen).enter_to_continue()