initial commit

loader.py

+import csv
+import re
+from cassandra.cluster import Cluster
+
+# CREATE KEYSPACE tlegluhe_metar  WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 2};
+# USE tlegluhe_metar;
+
+
+cluster = Cluster(['localhost'])
+session = cluster.connect('tlegluhe_metar')
+csvfilename = 'TP/asos.txt'
+limit = 200000
+
+def loadata(filename):
+	dateparser = re.compile(
+	
+		"(?P<year>\d+)-(?P<month>\d+)-(?P<day>\d+) (?P<hour>\d+):(?P<minute>\d+)"
+	)
+	with open(filename) as f:
+		for r in csv.DictReader(f):
+			match_date = dateparser.match(r["valid"])
+			
+			if not match_date:
+				continue
+			date = match_date.groupdict()
+			try:
+				data = {}
+				data["station"] = r["station"]
+				data["date"] = (
+					int(date["year"]),
+					int(date["month"]),
+					int(date["day"]),
+					int(date["hour"]),
+					int(date["minute"]),
+				)
+				data["lon"] = float(r["lon"])
+				data["lat"] = float(r["lat"])
+				
+				data["tmpf"] = float(r["tmpf"]) if (r["tmpf"] != 'M') else None
+				data["dwpf"] = float(r["dwpf"]) if (r["dwpf"] != 'M') else None
+				data["relh"] = float(r["relh"]) if (r["relh"] != 'M') else None
+				data["drct"] = float(r["drct"]) if (r["drct"] != 'M') else None
+				data["sknt"] = float(r["sknt"]) if (r["sknt"] != 'M') else None
+				data["p01i"] = float(r["p01i"]) if (r["p01i"] != 'M') else None
+				data["alti"] = float(r["alti"]) if (r["alti"] != 'M') else None
+				data["mslp"] = float(r["mslp"]) if (r["mslp"] != 'M') else None
+				data["vsby"] = float(r["vsby"]) if (r["vsby"] != 'M') else None
+				data["gust"] = float(r["gust"]) if (r["gust"] != 'M') else None
+				data["skyc1"] = r["skyc1"] if (r["skyc1"] != 'M') else None
+				data["skyc2"] = r["skyc2"] if (r["skyc2"] != 'M') else None
+				data["skyc3"] = r["skyc3"] if (r["skyc3"] != 'M') else None
+				data["skyc4"] = r["skyc4"] if (r["skyc4"] != 'M') else None
+				data["skyl1"] = float(r["skyl1"]) if (r["skyl1"] != 'M') else None
+				data["skyl2"] = float(r["skyl2"]) if (r["skyl2"] != 'M') else None
+				data["skyl3"] = float(r["skyl3"]) if (r["skyl3"] != 'M') else None
+				data["skyl4"] = float(r["skyl4"]) if (r["skyl4"] != 'M') else None
+				data["wxcodes"] = r["wxcodes"] if (r["wxcodes"] != 'M') else None
+				data["ice_accretion_1hr"] = float(r["ice_accretion_1hr"]) if (r["ice_accretion_1hr"] != 'M') else None
+				data["ice_accretion_3hr"] = float(r["ice_accretion_3hr"]) if (r["ice_accretion_3hr"] != 'M') else None
+				data["ice_accretion_6hr"] = float(r["ice_accretion_6hr"]) if (r["ice_accretion_6hr"] != 'M') else None
+				data["peak_wind_gust"] = float(r["peak_wind_gust"]) if (r["peak_wind_gust"] != 'M') else None
+				data["peak_wind_drct"] = float(r["peak_wind_drct"]) if (r["peak_wind_drct"] != 'M') else None
+				data["peak_wind_time"] = r["peak_wind_time"] if (r["peak_wind_time"] != 'M') else None
+				data["feel"] = float(r["feel"]) if (r["feel"] != 'M') else None
+				data["metar"] = r["metar"] if (r["metar"] != 'M') else None
+				
+				yield data
+			except TypeError:
+				continue
+				
+def limiteur(g, limit):
+	for i, d in enumerate(g):
+		if i >= limit:
+			return None
+		yield d
+		
+
+create_query_1 = '''CREATE TABLE finlande1(station text, annee int, mois int, jour int, heure int, min int, lat float, lon float, tmpf float, dwpf float, relh float, drct float, sknt float, p01i float, alti float, mslp float, vsby float, gust float, skyc1 text, skyc2 text, skyc3 text, skyc4 text, skyl1 float, skyl2 float, skyl3 float, skyl4 float, wxcodes text, ice_accretion_1hr float, ice_accretion_3hr float, ice_accretion_6hr float, peak_wind_gust float, peak_wind_drct float, peak_wind_time text,feel float, metar text, primary key((station, annee), mois, jour, heure, min) );'''
+	
+create_query_2 = '''CREATE TABLE finlande2(station text, annee int, mois int, jour int, heure int, min int, lat float, lon float, tmpf float, dwpf float, relh float, drct float, sknt float, p01i float, alti float, mslp float, vsby float, gust float, skyc1 text, skyc2 text, skyc3 text, skyc4 text, skyl1 float, skyl2 float, skyl3 float, skyl4 float, wxcodes text, ice_accretion_1hr float, ice_accretion_3hr float, ice_accretion_6hr float, peak_wind_gust float, peak_wind_drct float, peak_wind_time text,feel float, metar text, primary key((annee, mois, jour, heure, min), lat, lon, station ));'''
+
+create_query_3 = '''CREATE TABLE finlande3(station text, annee int, mois int, jour int, heure int, min int, lat float, lon float, tmpf float, dwpf float, relh float, drct float, sknt float, p01i float, alti float, mslp float, vsby float, gust float, skyc1 text, skyc2 text, skyc3 text, skyc4 text, skyl1 float, skyl2 float, skyl3 float, skyl4 float, wxcodes text, ice_accretion_1hr float, ice_accretion_3hr float, ice_accretion_6hr float, peak_wind_gust float, peak_wind_drct float, peak_wind_time text,feel float, metar text, primary key((annee), mois, jour, heure, min));'''
+
+def writecassandra(csvfilename, limit, session, tablename):
+	g = loadata(csvfilename)
+	data = limiteur(g, limit)
+	for r in data:
+		t = (
+
+					r["station"],
+					
+					r["date"][0],
+					r["date"][1],
+					r["date"][2],
+					r["date"][3],
+					r["date"][4],
+					
+					r["lat"],
+					r["lon"],
+					r["tmpf"],
+					r["dwpf"],
+					r["relh"],
+					r["drct"],
+					r["sknt"],
+					r["p01i"],
+					r["alti"],
+					r["mslp"],
+					r["vsby"],
+					r["gust"],
+					r["skyc1"],
+					r["skyc2"],
+					r["skyc3"],
+					r["skyc4"],
+					r["skyl1"],
+					r["skyl2"],
+					r["skyl3"],
+					r["skyl4"],
+					r["wxcodes"],
+					r["ice_accretion_1hr"],
+					r["ice_accretion_3hr"],
+					r["ice_accretion_6hr"],
+					r["peak_wind_gust"],
+					r["peak_wind_drct"],
+					r["peak_wind_time"],
+					r["feel"],
+					r["metar"]
+					
+					)	
+		query = """
+		INSERT INTO """+tablename+"""(
+
+			station,
+			annee,
+			mois,
+			jour,
+			heure,
+			min,
+			lat,
+			lon,
+			tmpf,
+			dwpf,
+			relh,
+			drct,
+			sknt,
+			p01i,
+			alti,
+			mslp,
+			vsby,
+			gust,
+			skyc1,
+			skyc2,
+			skyc3,
+			skyc4,
+			skyl1,
+			skyl2,
+			skyl3,
+			skyl4,
+			wxcodes,
+			ice_accretion_1hr,
+			ice_accretion_3hr,
+			ice_accretion_6hr,
+			peak_wind_gust,
+			peak_wind_drct,
+			peak_wind_time,
+			feel,
+			metar)
+		VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+		"""
+		session.execute(query, t)
+			
+
+session.execute('DROP TABLE IF EXISTS finlande1')
+session.execute(create_query_1)	
+writecassandra(csvfilename,limit,session, 'finlande1')
+
+session.execute('DROP TABLE IF EXISTS finlande2')
+session.execute(create_query_2)	
+writecassandra(csvfilename,limit,session,'finlande2')
+
+session.execute('DROP TABLE IF EXISTS finlande3')
+session.execute(create_query_3)	
+writecassandra(csvfilename,limit,session,'finlande3')
\ No newline at end of file

question1.py

+#POur faire marcher fonction, tapper:
+# %run TP/question1 EFKI 2003 2004 tmpf
+
+from cassandra.cluster import Cluster
+from pyspark import SparkContext
+from functools import reduce
+from pyspark.sql.functions import avg
+import numpy as np
+import sys
+import matplotlib.pyplot as plt
+import itertools
+import config
+
+cluster = Cluster(['localhost'])
+session = cluster.connect('tlegluhe_metar')
+session.default_fetch_size = 100
+sc = SparkContext.getOrCreate()
+
+numericindicators = ['tmpf', 'dwpf', 'relh', 'drct', 'sknt', 'p01i', 'alti', 'mslp', 'vsby', 'gust', 'feel', 'ice_accretion_1hr', 'ice_accretion_3hr', 'ice_accretion_6hr', 'peak_wind_gust', 'peak_wind_drct']
+
+if __name__ == "__main__":
+	param = "tmpf"
+	annee_deb = 2003
+	annee_fin = 2012
+	point = "EFKI"
+
+	if len(sys.argv) > 1:
+		point = sys.argv[1]
+
+	if len(sys.argv) > 2:
+		if(int(sys.argv[2]) > 2002 and int(sys.argv[2])	< 2013):
+			annee_deb = int(sys.argv[2])
+
+	if len(sys.argv) > 3:
+		if(int(sys.argv[3]) > 2002 and int(sys.argv[3]) < 2013 and int(sys.argv[3]) > int(sys.argv[2]) ):
+			annee_fin = int(sys.argv[3])
+
+	if len(sys.argv) > 4:
+		if(sys.argv[4] in numericindicators):
+			param = sys.argv[4]
+		else:
+			print('You can\'t choose this indicator for this question')
+
+data = []
+for annee in range(annee_deb, annee_fin+1):
+
+	requete = """SELECT station, annee, mois, jour, {}
+					FROM finlande1 where station = '{}' AND annee = {};
+			""".format(param, point, annee)
+	
+	data = itertools.chain(data, session.execute(requete))
+	
+
+
+D0 = sc.parallelize(data)
+
+D0 = D0.filter(lambda d: (d[4] is not None))
+
+
+if((annee_fin - annee_deb) <= 2):
+
+	# PREMIER RESULTAT : GRAPHE SUR LA DUREE CHOISIE PAR MOIS
+	#Calcul moyenne, min, max et écart type par mois et par année
+	D = D0.map( lambda d:((d[1],d[2]), np.array([1,d[4], d[4], d[4], d[4]**2])))
+	D = D.reduceByKey( lambda a,b:(a[0]+b[0],a[1]+b[1],min(a[2],b[2]),max(a[3],b[3]), a[4]+b[4]))
+
+
+	D = D.map(lambda d: ((d[0][0], d[0][1]), d[1][1]/d[1][0], d[1][2], d[1][3],
+								np.sqrt(-(d[1][1]/d[1][0])**2+d[1][4]/d[1][0])))
+
+		
+
+	#creation tableau de données					   
+	data_month = np.zeros(((annee_fin - annee_deb + 1)* 12))
+	data_min = np.zeros(((annee_fin - annee_deb + 1)* 12))
+	data_max = np.zeros(((annee_fin - annee_deb + 1)* 12))
+	data_sqrt = np.zeros(((annee_fin - annee_deb + 1)* 12))
+	legend_month = [None] * ((annee_fin - annee_deb + 1)* 12)
+
+
+	for row in D.collect():
+		data_month[(row[0][0] - annee_deb)*12 + row[0][1]-1] = row[1]
+		data_min[(row[0][0] - annee_deb)*12 + row[0][1]-1] = row[2]
+		data_max[(row[0][0] - annee_deb)*12 + row[0][1]-1] = row[3]
+		data_sqrt[(row[0][0] - annee_deb)*12 + row[0][1]-1] = row[4]
+		legend_month[(row[0][0] - annee_deb)*12 + row[0][1]-1] = str(str(row[0][1]) + "/" + str(row[0][0]))
+
+
+	plt.clf()
+	plt.cla()
+
+	x = np.arange((annee_fin - annee_deb + 1)* 12)
+	plt.subplot(221)
+	plt.bar(x, height= data_month) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Moyenne ' + param)
+	plt.title('Moyenne par mois pour ' + point)
+
+	x = np.arange((annee_fin - annee_deb + 1)* 12)
+	plt.subplot(222)
+	plt.bar(x, height= data_min) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Min ' + param)
+	plt.title('Minimum par mois pour ' + point)
+
+	x = np.arange((annee_fin - annee_deb + 1)* 12)
+	plt.subplot(223)
+	plt.bar(x, height= data_max) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Moyenne ' + param)
+	plt.title('Maximum par mois pour '+ point)
+
+	x = np.arange((annee_fin - annee_deb + 1)* 12)
+	plt.subplot(224)
+	plt.bar(x, height= data_sqrt) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Ecart type ' + param)
+	plt.title('Ecart type par mois pour ' + point)
+	plt.subplots_adjust(wspace=0.5, hspace=0.5)
+	plt.savefig("data_general.pdf")
+	
+	
+
+if((annee_fin - annee_deb) > 2):
+
+	# PREMIER RESULTAT : GRAPHE SUR LA DUREE CHOISIE PAR MOIS
+	#Calcul moyenne, min, max et écart type par mois et par année
+	D = D0.map( lambda d:((d[1]), np.array([1,d[4], d[4], d[4], d[4]**2])))
+	D = D.reduceByKey( lambda a,b:(a[0]+b[0],a[1]+b[1],min(a[2],b[2]),max(a[3],b[3]), a[4]+b[4]))
+
+
+	D = D.map(lambda d: ((d[0]), d[1][1]/d[1][0], d[1][2], d[1][3],
+								np.sqrt(-(d[1][1]/d[1][0])**2+d[1][4]/d[1][0])))
+
+	
+
+	#creation tableau de données					   
+	data_month = np.zeros((annee_fin - annee_deb + 1))
+	data_min = np.zeros((annee_fin - annee_deb + 1))
+	data_max = np.zeros((annee_fin - annee_deb + 1))
+	data_sqrt = np.zeros((annee_fin - annee_deb + 1))
+	legend_month = [None] * ((annee_fin - annee_deb + 1))
+
+
+	for row in D.collect():
+		data_month[(row[0] - annee_deb)] = row[1]
+		data_min[(row[0] - annee_deb)] = row[2]
+		data_max[(row[0] - annee_deb)] = row[3]
+		data_sqrt[(row[0] - annee_deb)] = row[4]
+		legend_month[(row[0] - annee_deb)] = str(row[0])
+		
+
+	plt.clf()
+	plt.cla()
+
+	x = np.arange((annee_fin - annee_deb + 1))
+	plt.subplot(221)
+	plt.bar(x, height= data_month) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Moyenne ' + param)
+	plt.title('Moyenne par an pour ' + point)
+
+	x = np.arange((annee_fin - annee_deb + 1))
+	plt.subplot(222)
+	plt.bar(x, height= data_min) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Min ' + param)
+	plt.title('Minimum par an pour ' + point)
+
+	x = np.arange((annee_fin - annee_deb + 1))
+	plt.subplot(223)
+	plt.bar(x, height= data_max) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Moyenne ' + param)
+	plt.title('Maximum par an pour '+ point)
+
+	x = np.arange((annee_fin - annee_deb + 1))
+	plt.subplot(224)
+	plt.bar(x, height= data_sqrt) 
+	plt.xticks(x, legend_month, fontsize=5, rotation=90)
+	plt.xlabel('')
+	plt.ylabel('Ecart type ' + param)
+	plt.title('Ecart type par an pour ' + point)
+	plt.subplots_adjust(wspace=0.5, hspace=0.5)
+	plt.savefig("data_general_an.pdf")	
+	
+
+
+
+#traitement par saison sur toutes les années:
+
+D2 = D0.map( lambda d:(((d[2]-1)//3), np.array([1,d[4], d[4], d[4], d[4]**2])))
+D2 = D2.reduceByKey( lambda a,b:(a[0]+b[0],a[1]+b[1],min(a[2],b[2]),max(a[3],b[3]), a[4]+b[4]))
+
+
+D2 = D2.map(lambda d: ((d[0]), d[1][1]/d[1][0], d[1][2], d[1][3],
+							   np.sqrt(-(d[1][1]/d[1][0])**2+d[1][4]/d[1][0])))
+
+data2_moy = np.zeros(4)
+data2_min = np.zeros(4)
+data2_max = np.zeros(4)
+data2_sqrt = np.zeros(4)
+legend_saison = ["Printemps","Ete","Automne","Hiver"]
+
+for row in D2.collect():
+	data2_moy[(row[0])] = row[1]
+	data2_min[(row[0])] = row[2]
+	data2_max[(row[0])] = row[3]
+	data2_sqrt[(row[0])] = row[4]
+
+plt.clf()
+plt.cla()
+
+x = np.arange(4)
+plt.subplot(221)
+plt.bar(x, height= data2_moy) 
+plt.xticks(x, legend_saison, fontsize=5, rotation=90)
+plt.xlabel('')
+plt.ylabel('Moyenne ' + param)
+plt.title('Moyenne par saison pour ' + point)
+
+x = np.arange(4)
+plt.subplot(222)
+plt.bar(x, height= data2_min) 
+plt.xticks(x, legend_saison, fontsize=5, rotation=90)
+plt.xlabel('')
+plt.ylabel('Min ' + param)
+plt.title('Minimum par saison pour ' + point)
+
+x = np.arange(4)
+plt.subplot(223)
+plt.bar(x, height= data2_max) 
+plt.xticks(x, legend_saison, fontsize=5, rotation=90)
+plt.xlabel('')
+plt.ylabel('Moyenne ' + param)
+plt.title('Maximum par saison pour '+ point)
+
+x = np.arange(4)
+plt.subplot(224)
+plt.bar(x, height= data2_sqrt) 
+plt.xticks(x, legend_saison, fontsize=5, rotation=90)
+plt.xlabel('')
+plt.ylabel('Ecart type ' + param)
+plt.title('Ecart type par saison pour ' + point)
+plt.subplots_adjust(wspace=0.5, hspace=0.5)
+plt.savefig("data_saison.pdf")
+
+	
\ No newline at end of file

question2.py

+#POur faire marcher fonction, tapper:
+# %run TP/question2 tmpf 2010 1 1 0 0
+
+from mpl_toolkits.basemap import Basemap
+import matplotlib.pyplot as plt
+import numpy as np
+from cassandra.cluster import Cluster
+from pyspark import SparkContext
+from functools import reduce
+from pyspark.sql.functions import avg
+import sys
+import config
+
+plt.cla()
+plt.clf()
+cluster = Cluster(['localhost'])
+session = cluster.connect('tlegluhe_metar')
+sc = SparkContext.getOrCreate()
+
+
+
+if __name__ == "__main__":
+	param = "tmpf"
+	annee=2003
+	mois=1
+	jour =1
+	heure=0
+	minute=20
+	
+	if len(sys.argv) > 1:	
+		param = sys.argv[1]
+	
+	if len(sys.argv) > 2:
+		if(int(sys.argv[2]) > 2002 and int(sys.argv[2]) < 2013):
+			annee= int(sys.argv[2])
+		
+	if len(sys.argv) > 3:	
+		if(int(sys.argv[3]) > 0 and int(sys.argv[3]) < 13):
+			mois= int(sys.argv[3])
+			
+	if len(sys.argv) > 4:	
+		if(int(sys.argv[4]) > 0 and int(sys.argv[4]) < 31 ):
+			jour= int(sys.argv[4])		
+	
+	if len(sys.argv) > 5:	
+		if(int(sys.argv[5]) >= 0 and int(sys.argv[5]) < 24 ):
+			heure= int(sys.argv[5])		
+	
+	if len(sys.argv) > 6:	
+		if(int(sys.argv[6]) >= 0 or int(sys.argv[6]) < 10 ):
+			minute = 0
+		elif(int(sys.argv[6]) >= 10 or int(sys.argv[6]) < 35 ):
+			minute = 20
+		elif(int(sys.argv[6]) >= 35 and int(sys.argv[6]) < 60 ):
+			minute = 50
+	
+		
+		
+
+
+	
+requete = """SELECT lat,lon,station,{}
+				FROM finlande2 where annee = {} AND mois = {} AND jour= {} AND heure = {} AND min = {}
+				LIMIT 5000
+				;
+		  """.format(param, annee, mois, jour, heure, minute)
+data = session.execute(requete)
+
+
+lats = []
+lons = []
+values = []
+
+for row in data:
+	if (row[0]!=None) & (row[0]>=58) & (row[0]<=72) & (row[1]!=None) & (row[1]>=20) & (row[1]<=32) & (row[3] != None):
+		lats.append(row[0])
+		lons.append(row[1])
+		if(param in numericindicators):
+			values.append(round(row[3], 2))
+		else:
+			values.append(row[3])
+
+ 
+map = Basemap(projection='merc', lat_0 = 65, lon_0 = 26,
+	resolution = 'h', area_thresh = 0.1,
+	llcrnrlon=20, llcrnrlat=59,
+	urcrnrlon=32, urcrnrlat=71)
+ 
+ 
+map.drawcountries()
+map.drawmapboundary(fill_color='#46bcec')
+map.fillcontinents(color = 'white',lake_color='#46bcec')
+ 
+x,y = map(lons, lats)
+map.plot(x, y, 'bo', markersize=3)
+
+labels = values
+
+ 
+for label, xpt, ypt in zip(labels, x, y):
+	plt.text(xpt+20000, ypt+20000, label, fontsize=6)
+plt.title(''+ param + ' à ' + str(heure) +'h'+ str(minute) + ' le ' + str(jour) +'/'+ str(mois) +'/'+ str(annee) + '')
+plt.savefig("carte_finlande.pdf")
+print("Done ! Check the file carte_finlande.pdf at the root of this project")

question3.py

+#Pour faire marcher cette fonction, tapper:
+# %run TP/question3 2009 2010 1 3 tmpf 
+
+from cassandra.cluster import Cluster
+import sys
+import numpy as np
+import glob
+from math import sqrt
+import random
+from pyspark import  SparkContext
+from functools import reduce
+import matplotlib.pyplot as plt
+from mpl_toolkits.basemap import Basemap
+import matplotlib.cm as cm
+import itertools
+import config
+
+plt.cla()
+plt.clf()
+
+cluster = Cluster(['localhost'])
+session = cluster.connect('tlegluhe_metar')
+session.default_fetch_size = 100
+sc = SparkContext.getOrCreate()
+
+
+def getIndicatorCluster():
+
+	data = []
+	for annee in range(annee_deb, annee_fin+1):
+		if( annee == annee_deb):
+			requete = """SELECT lat,lon, {}
+							FROM finlande3 where annee = {} and mois >= {} ;
+					""".format(param, annee, mois_deb)
+			
+			data = itertools.chain(data, session.execute(requete))
+			
+		elif( annee == (annee_fin + 1)):
+			requete = """SELECT lat,lon, {}
+							FROM finlande3 where annee = {} and mois <= {} ;
+					""".format(param, annee, mois_fin)
+			
+			data = itertools.chain(data, session.execute(requete))
+			
+		else:
+			requete = """SELECT lat,lon, {}
+							FROM finlande3 where annee = {};
+					""".format(param, annee)
+			
+			data = itertools.chain(data, session.execute(requete))
+			
+	for dat in data:
+			flag = True