def filter_simple_wifi(params):
params['subject'] = "Filter WIFI Graphic "
email_response = utils.show_info(params) + "\n"
path_real, database_name = utils.download_database(params['filename'], full_path = False)
only_name = database_name[:database_name.rfind(".")]
params["all_time"] = utils.get_datetime() #TIME
params["a_func"] = utils.get_datetime() #TIME
try:
email_response += "Exporting data (WIFI) ..."
export_csv ="filter_wifi%s" % (only_name+".csv")
wifi_list_mac, wifi_list_name = utils.parse_wifi_list(params['wifi_list'])
ExportWifi(wifi_list_mac,wifi_list_name,params['is_blacklist']).run(path_real+database_name,path_real+export_csv)
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("CSV conversion",params["a_func"])
email_response += "OK\n"
email_response += "\n CSV file = "+utils.create_link(params['KEY_IML'],str(path_real),str(export_csv))+"\n\n\n"
email_response += time_txt
try:
email_response += "Building Graphic (Bluetooth) ..."
path_graphic ="filter_wifi_%s" % (only_name+".pdf")
WifiGraphic().run(path_real+export_csv,path_real+path_graphic)
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("Graphic creation",params["a_func"])
email_response += "OK\n"
email_response += "\n Graphic = "+utils.create_link(params['KEY_IML'],str(path_real),str(path_graphic))+"\n\n\n"
email_response += time_txt
email_response += utils.end_func("All process",params["all_time"])
return utils.response_email(params['email'],params['subject'], email_response)
def simple_sensorHub(params):
params['subject'] = "SensorHub Graphic "
email_response = ""
path_real, database_name = utils.download_database(params['filename'], full_path = False)
only_name = database_name[:database_name.rfind(".")]
params["all_time"] = utils.get_datetime() #TIME
params["a_func"] = utils.get_datetime() #TIME
try:
email_response += "Exporting data (Sensor Hub) ..."
export_csv ="SensorHub_%s" % (only_name+".csv")
ExportSensorHub().run(path_real+database_name, path_real+export_csv)
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("CSV conversion",params["a_func"])
email_response += "OK\n"
email_response += "\n CSV file = "+utils.create_link(params['KEY_IML'],str(path_real),str(export_csv))+"\n\n\n"
email_response += time_txt
try:
email_response += "Building Graphic (Sensor Hub) ..."
path_graphic ="sensor_hub_%s" % (only_name+".pdf")
SensorHubGraphic().run(path_real+export_csv,path_real+path_graphic)
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("Graphic creation",params["a_func"])
email_response += "OK\n"
email_response += "\n Graphic = "+utils.create_link(params['KEY_IML'],str(path_real),str(path_graphic))+"\n\n\n"
email_response += time_txt
email_response += utils.end_func("All process",params["all_time"])
return utils.response_email(params['email'],params['subject'], email_response)
def continue_ML(params):
params["email_response"] = utils.show_info(params) + "\n"
params["only_database_name"] = params['filename'][:params['filename'].rfind(".")]
params["subject"] = "Machine Learning - Results (Continue)"
params["all_time"] = utils.get_datetime() #TIME
if params['level_html'] == 1:
params['csvfile'] = params['filename']
df = E_pre_processing(params)
E_clustering(df,params)
E_graphic(params)
elif params['level_html'] == 2:
params['csvpreprocessing'] = params['filename']
df = pd.read_csv(open(params['path_real']+params["csvpreprocessing"],"r"), sep=',', header=0, index_col=0)
E_clustering(df,params)
E_graphic(params)
elif params['level_html'] == 3:
params['csvcluster'] = params['filename']
E_graphic(params)
params["email_response"] += utils.end_func("All process",params["all_time"])
print params["email_response"]
return utils.response_email(params['email'],params["subject"], params["email_response"])
def E_graphic(params):
params["a_func"] = utils.get_datetime() #TIME
try:
params["email_response"] += "Creating graphic..."
params['pdfgraphic'] = (params["only_database_name"]+".pdf").replace("(","").replace(")","")
print "Rscript machine_learning/pdf/pdf_lines.R \""+params['path_real']+params['csvcluster']+"\" \""+params['path_real']+params['pdfgraphic']+"\""
os.system("Rscript machine_learning/pdf/pdf_lines.R \""+params['path_real']+params['csvcluster']+"\" \""+params['path_real']+params['pdfgraphic']+"\"")
except Exception as e:
utils.get_error(e, params)
raise
time_txt = utils.end_func("Graphic creation",params["a_func"])
params["email_response"] += "OK\n"
params["email_response"] += "\n Graphic = "+utils.create_link(params['KEY_IML'],str(params['path_real']),str(params['pdfgraphic']))+"\n\n"
params["email_response"] += time_txt
def exec_ML(params):
params["email_response"] = utils.show_info(params) + "\n"
params["subject"] = "Machine Learning - Results (With Filter)"
E_get_database(params)
E_export_csv(params)
df = E_pre_processing(params)
E_clustering(df,params)
E_graphic(params)
params["email_response"] += utils.end_func("All process",params["all_time"])
# print params["email_response"]
return utils.response_email(params['email'],params["subject"], params["email_response"])
def E_clustering(df,params):
params["a_func"] = utils.get_datetime() #TIME
try:
params["email_response"] += "Clustering ..."
labels, n_clusters = Clustering(df, mode="fixed_k", n_clusters=int(params['number'])).clusterize()
timestamp = list(df.index)
params['csvcluster'] = str(params['number'])+"clusters"+params["only_database_name"]+".csv"
with open(params['path_real']+params['csvcluster'], 'w') as f:
f.write("timestamp,clusters\n")
for i in range(len(labels)):
f.write("{},{}\n".format(timestamp[i],labels[i]))
except Exception as e:
utils.get_error(e, params)
raise
time_txt = utils.end_func("Clustering",params["a_func"])
params["email_response"] += "OK\n"
params["email_response"] += "\n CSV MachineLearning = "+utils.create_link(params['KEY_IML'],str(params['path_real']),str(params['csvcluster']),str("3"),{})+"\n\n"
params["email_response"] += time_txt
def E_pre_processing(params):
params["a_func"] = utils.get_datetime() #TIME
try:
params["email_response"] += "PreProcessing ..."
params['csvpreprocessing'] = "pre_processing"+params["csvfile"]
df = pd.read_csv(open(params['path_real']+params["csvfile"],"r"), sep=',', header=0, index_col=0)
pre_processing = pre.PreProcessing(df,norm=params['optimzation_sensor_hub'])
df = pre_processing.build()
df.to_csv(params['path_real']+params['csvpreprocessing'], sep=',', encoding='utf-8', header=True)
except Exception as e:
utils.get_error(e, params)
raise
time_txt = utils.end_func("PreProcessing",params["a_func"])
params["email_response"] += "OK\n"
params["email_response"] += "\n CSV PreProcessing = "+utils.create_link(params['KEY_IML'],str(params['path_real']),str(params['csvpreprocessing']),str("2"),{'optimzation_sensor_hub':params["optimzation_sensor_hub"]})+"\n\n"
params["email_response"] += time_txt
return df
def E_export_csv(params):
init_time = None
finish_time = None
params["whitelist_ble"]=[]
params["whitelist_wifi"]=[]
params["blacklist_ble"]=[]
params["blacklist_wifi"]=[]
if params['bluetooth']:
if 'is_only_hardcode' in params:
if params['is_only_hardcode']:
# Hardcode
print "entro errado"
else:
print "entro certo"
if 'ble_list' in params:
if params['ble_list']:
if 'is_blacklist_ble' in params:
if params['is_blacklist_ble']:
params["blacklist_ble"] = params['ble_list']
else:
params["whitelist_ble"] = params['ble_list']
if params['wifi']:
if 'wifi_list' in params:
if params['wifi_list']:
if 'is_blacklist_wifi' in params:
if params['is_blacklist_wifi']:
params["blacklist_wifi"] = utils.parse_wifi_list(params['wifi_list'])[0]
else:
params["whitelist_wifi"] = utils.parse_wifi_list(params['wifi_list'])[0]
if 'set_period' in params:
if params['set_period']:
init_time = params['init_time']
finish_time = params['finish_time']
params["a_func"] = utils.get_datetime() #TIME
try:
params["email_response"] += "Converting csv ..."
params["csvfile"] = params["only_database_name"]+".csv"
export_csv.run(
inputfile=params['path_real']+params['database_name'],
outputfile=params['path_real']+params["csvfile"],
bluetooth=params['bluetooth'],
wifi=params['wifi'],
sensorhub=params['optimzation_sensor_hub'],
battery= True if params['optimzation_sensor_hub'] else False,
optimize=params['optimzation_sensor_hub'],
whitelist_ble=params["whitelist_ble"],
whitelist_wifi=params["whitelist_wifi"],
blacklist_ble=params["blacklist_ble"],
blacklist_wifi=params["blacklist_wifi"],
init_time = init_time,
finish_time = finish_time,
)
except Exception as e:
utils.get_error(e, params)
raise
time_txt = utils.end_func("CSV conversion",params["a_func"])
params["email_response"] += "OK\n"
params["email_response"] += "\n CSV file = "+utils.create_link(params['KEY_IML'],str(params['path_real']),str(params["csvfile"]),str("1"),{'optimzation_sensor_hub':params["optimzation_sensor_hub"]})+"\n\n"
params["email_response"] += time_txt
def exec_ML_1(params):
params['subject'] = "Machine Learning - Results"
email_response = utils.show_info(params) + "\n"
try:
email_response += "download database ..."
path_real, database_name = utils.download_database(params['filename'], full_path = False)
except Exception as e:
return utils.get_error(e, params)
email_response += "OK\n"
email_response += "\n Database = "+utils.create_link(params['KEY_IML'],str(path_real),str(database_name))+"\n\n\n"
params['path_real'] = path_real
params['database_name'] = database_name
params["only_database_name"] = database_name[:database_name.rfind(".")]
params["all_time"] = utils.get_datetime() #TIME
params["a_func"] = utils.get_datetime() #TIME
try:
email_response += "Converting csv ..."
params["csvfile"] = params["only_database_name"]+".csv"
export_csv.run(
inputfile=path_real+database_name,
outputfile=path_real+params["csvfile"],
bluetooth=params['bluetooth'],
wifi=params['wifi'],
sensorhub=params['optimzation_sensor_hub'],
battery=True if params['optimzation_sensor_hub'] else False ,
optimize=params['optimzation_sensor_hub'],
)
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("CSV conversion",params["a_func"])
email_response += "OK\n"
email_response += "\n CSV file = "+utils.create_link(params['KEY_IML'],str(path_real),str(params["csvfile"]))+"\n\n"
email_response += time_txt
params["a_func"] = utils.get_datetime() #TIME
try:
email_response += "PreProcessing ..."
df = pd.read_csv(open(path_real+params["csvfile"],"r"), sep=',', header=0, index_col=0)
pre_processing = pre.PreProcessing(df,norm=params['optimzation_sensor_hub'])
df = pre_processing.build()
df.to_csv(path_real+"pre_processing"+params["csvfile"], sep=',', encoding='utf-8', header=True)
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("PreProcessing",params["a_func"])
email_response += "OK\n"
email_response += "\n CSV PreProcessing = "+utils.create_link(params['KEY_IML'],str(path_real),path_real+"pre_processing"+params["csvfile"])+"\n\n"
email_response += time_txt
params['csvpreprocessing'] = path_real+"pre_processing"+params["csvfile"]
params["a_func"] = utils.get_datetime() #TIME
try:
email_response += "Clustering ..."
labels, n_clusters = Clustering(df, mode="fixed_k", n_clusters=int(params['number'])).clusterize()
timestamp = list(df.index)
params['csvcluster'] = str(params['number'])+"clusters"+params["only_database_name"]+".csv"
with open(path_real+params['csvcluster'], 'w') as f:
f.write("timestamp,clusters\n")
for i in range(len(labels)):
f.write("{},{}\n".format(timestamp[i],labels[i]))
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("Clustering",params["a_func"])
email_response += "OK\n"
email_response += "\n CSV MachineLearning = "+utils.create_link(params['KEY_IML'],str(path_real),str(params['csvcluster']))+"\n\n"
email_response += time_txt
params["a_func"] = utils.get_datetime() #TIME
try:
email_response += "Creating graphic..."
params['pdfgraphic'] = (params["only_database_name"]+".pdf").replace("(","").replace(")","")
print "Rscript machine_learning/pdf/pdf_lines.R \""+path_real+params['csvcluster']+"\" \""+path_real+params['pdfgraphic']+"\""
os.system("Rscript machine_learning/pdf/pdf_lines.R \""+path_real+params['csvcluster']+"\" \""+path_real+params['pdfgraphic']+"\"")
except Exception as e:
return utils.get_error(e, params)
time_txt = utils.end_func("Graphic creation",params["a_func"])
email_response += "OK\n"
email_response += "\n Graphic = "+utils.create_link(params['KEY_IML'],str(path_real),str(params['pdfgraphic']))+"\n\n"
email_response += time_txt
print email_response
email_response += utils.end_func("All process",params["all_time"])
return utils.response_email(params['email'],"Machine Learning - Results", email_response)
