MatheusrdSantos

todo_lst = [(0, SEED)]
todo_set = set(SEED)
done_set = set()

g = nx.DiGraph()
layer, page = todo_lst[0]

# a consulta será realizada até o nível 1
while layer < 2:
  # remove a página atual da lista de páginas pendentes

MatheusrdSantos

CREATE KEYSPACE cities WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 2};

CREATE TABLE IF NOT EXISTS city_per_country (
  id uuid,
  name text,
  country text,
  state text,
  population int,
  PRIMARY KEY (country, population, id)
 ) WITH CLUSTERING ORDER BY (population DESC);

MatheusrdSantos

CREATE DATABASE IF NOT EXISTS cities_db;

CREATE TABLE IF NOT EXISTS Countries (
    id int NOT NULL AUTO_INCREMENT,
    name varchar(255) NOT NULL,
    PRIMARY KEY (id)
);
CREATE TABLE IF NOT EXISTS States (
    id int NOT NULL AUTO_INCREMENT,

MatheusrdSantos

version: "2.3"
services:
  mysql1:
    image: mysql
    container_name: mysql1
    hostname: mysql1
    mem_limit: 6144M
    mem_reservation: 1536M
    ports:
      - "3306:3306"

MatheusrdSantos

version: "2.3"
networks:
  cassandra-final:
services:
  cass1:
    image: cassandra
    container_name: cass1
    hostname: cass1
    mem_limit: 2048M
    mem_reservation: 512M

MatheusrdSantos

fig, ax = plt.subplots(nrows=1,ncols=6,figsize=(25,4))
states = ['SP', 'RJ', 'AM', 'CE', 'BA', 'RN']

for i, state in enumerate(states):
    state_data = data_br_merged.loc[data_br_merged['state']==state]
    data_moving_average = state_data.rolling(window=7).mean().dropna()
    data_mean = data_moving_average.apply(normalize, df=data_moving_average, axis=1)[['residential_normalized', 'new_cases_normalized']]
    data_mean.plot(legend=True, ax=ax[i])
    ax[i].set_title(state,fontsize=12,ha='right')
    ax[i].set_xticklabels(["", "Mar","", "", "", "Oct"])

MatheusrdSantos

# iterate over the selected states and prepare the plot
for i, state in enumerate(states):
    other_states = data_br_unstack[item].rolling(window=7).mean().dropna()
    # the dates must be converted to integers
    other_states.index = mdates.date2num(other_states.index)
    other_states.plot(legend=False,color=[(0.7,0.7,0.7)],linewidth=1, alpha=0.4, ax=ax[i])
    
    selected_state = data_br_unstack[item][state].rolling(window=7).mean().dropna()
    # the dates must be converted to integers
    selected_state.index = mdates.date2num(selected_state.index)