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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Initial Considerations
# Samples are collected on the server every ten minutes (144 samples/day)
# Imports
# Libraries and custom classes
import re
import os
import sys
import numpy as np
from datetime import datetime
import datetime
from pandas import concat,DataFrame
import csv
from pandas import concat,DataFrame
import pandas.core.frame # read_csv
from numpy import concatenate
from keras.models import load_model
from sklearn.preprocessing import LabelEncoder
class Utils:
city = ""
dir_pat = "" # Current working directory
def __init__(self, city=""):
if len(city) == 0:
sys.exit("Missing city in the initialization")
self.dir_path = os.path.dirname(os.path.realpath(__file__)) = city
def read(self, param):
with open(self.dir_path + "/config/" + param) as f:
content = f.readlines()
# you may also want to remove whitespace characters like `\n` at the end of each line
content = [int(x.strip()) for x in content]
return content
def stations_from_web(self, city):
Parse the JSON/XML feed and return the staions
- Returns: Pandas Dataframe [idStation, stationName, latitude, longitude]
import urllib
import json
import time
import codecs
import requests
urls = {"Barcelona": "",
"Santander": "",
"Bilbao": "",
"Chicago": "",
"Bilbao": "",
"Madrid": "",
"Vienna": ""}
data = requests.get(urls[city]).json()
# Filter and only show the stations, the feeds contain more data than necessary.
if city == "Bilbao":
data = data["countries"][0]["cities"][0]["places"]
elif city == "Chicago":
data = data["stationBeanList"]
elif city == "Madrid":
data = data["network"]["stations"]
elif city== "Barcelona":
data = data["network"]["stations"]
elif city== "Vienna":
data = data["network"]["stations"]
feedKeywords = {"Santander": ["number", "name", "lat", "lng"],
"Chicago": ["id", "stationName", "latitude", "longitude"],
"Bilbao": ["uid", "name", "lat", "lng"],
"Madrid": ["id", "name", "latitude", "longitude"],
"Barcelona": ["id", "name", "latitude", "longitude"],
"Vienna": ["id", "name", "latitude", "longitude"]}
idVAR = feedKeywords[city][0]
nameVAR = feedKeywords[city][1]
latVAR = feedKeywords[city][2]
lonVAR = feedKeywords[city][3]
query = ""
totalQuery = ""
current_time = time.strftime('%Y-%m-%dT%H:%M:%SZ',time.localtime(time.time()))
totalQuery += "idstation,nom,lat,lon\n"
pre_df = []
for i in data:
totalQuery += query
identifier = str(i[idVAR])
name = str(i[nameVAR])
if city == "Santander":
latitude = str(i['position'][latVAR])
longitude = str(i['position'][lonVAR])
latitude = str(i[latVAR])
longitude = str(i[lonVAR])
if city == "Bilbao":
if'\d\d-\w+', name): # any words end with ing?
name = name[3::]
pre_df.append([identifier, name, latitude, longitude])
df = DataFrame(pre_df, columns = ['idstation', 'nom', 'lat', 'lon'])
print("> There are " + str(df.shape[0]) + " stations in " + str(city))
return df
# Reads the list in the PATH and returns a LIST
def read_csv_as_list(self, path):
data = []
with open(path) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
for row in readCSV:
data = row
return data
# Checks if de current directory exists, if not it's created
# Directory is a list of strings
def check_and_create(self, directory):
for path in directory:
if not os.path.exists(self.dir_path + path):
os.makedirs(self.dir_path + path)
# Save an array/list/... for future debugging
def save_array_txt(self, path, array):
# Guardar array con la función nativa de NumPy
if type(array) is np.ndarray:
np.savetxt(path, array, delimiter=',', fmt='%.0f')
# Guardar LabelEncoders como una lista con cada elemento codificado en una linea
elif type(array) is LabelEncoder:
f = open(path, 'w' )
for i in range(len(array.classes_)):
f.write('{:>4}'.format(i) + " " + str(array.classes_[i]) + "\n")
elif type(array) is DataFrame:
array.to_csv(path, sep=',')
elif type(array) is list:
with open(path,"w+") as f:
wr = csv.writer(f,delimiter=",")
with open(path, 'w+', newline='\n') as myfile:
for element in array:
myfile.write(str(element) + "\n")
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