Little change

question2.py

@@ -2,167 +2,9 @@ import numpy as np
 import matplotlib.pyplot as plt
 from functools import reduce
 from database_pre2 import connection
-import matplotlib.pyplot as plt
 import re
 import folium
 
-def add (x,y):
-    return x+y
-
-
-def abs_diff(x,y):
-    return abs(x-y)
-
-def diff(x,y):
-    return x-y
-
-
-
-#caculate mean reduce
-#input [count,mean]
-def reduceFonction (x,y):
-    result = []
-    for i in range(2):
-        result.append(reduce(add,[x[i],y[i]]))
-    return result
-
-#input [valeur] -> [count,mean]
-def mapFonction1 (x):
-    return [1,x]
-
-#input [count,mean] -> [mean]
-def mapFonction2 (x):
-    return x[1]/x[0]
-
-
-def testNan (x):
-    test = x != x
-    return test
-
-
-def mapReduce_kmeans(data,targetNB):
-    results = dict()
-    for row in data.result():
-        data_target = row[targetNB]
-        if testNan(data_target):
-            continue
-        data_espace = (row[1],row[2],row[3])
-        if results.get(data_espace) is None:
-            results[data_espace] = mapFonction1(data_target)
-        else:
-            mapresult = mapFonction1(data_target)
-            results[data_espace] = reduceFonction(mapresult,results[data_espace])
-    for eachEspace in results:
-        results[eachEspace] = mapFonction2(results[eachEspace])
-    return results
-
-
-def cluster_nb_diff(centre_new,centre):
-    sum = 0
-    for i in range(3):
-        sum += abs(centre_new[i][0]-centre[i][0])
-    return sum/3
-
-
-
-#input [tmpt] -> [tmpt,tmpt,tmpt,tmpt]
-def map1_kmeans(x):
-    return [x,x,x,x]
-
-def mapCentre(x):
-    return [x[0],x[1],x[2],0]
-
-#input [tmpt,tmpt,tmpt,tmpt] and [c1,c2,c3,0] -> [|tmpt - c1|,|tmpt - c2|,|tmpt - c3|,tmpt]
-
-
-def reduceKmeans (x,y):
-    result = []
-    for i in range(4):
-        result.append(reduce(abs_diff,[x[i],y[i]]))
-    return result
-
-
-#input [|tmpt - c1|,|tmpt - c2|,|tmpt - c3|,tmpt] -> [cluster number, min(|tmpt - c|), tmpt]
-def map2_kmeans(x):
-    min_value = 10000000000000
-    index = 0
-    for each in range(3):
-        if min_value > x[each]:
-            min_value = x[each]
-            index = each
-    return [index,min_value,x[3]]
-
-
-
-
-def MapnewCentre(x):
-    return x[1]/x[0]
-
-
-
-def kmeans (data,targetNB):
-    #3centre with [point count, temprature centre]
-    centre = {0:[1,0],1:[1,0],2:[1,0]}
-    #cluster est pour stocler lat, lon de chaque point de chaque cluster
-    cluster = [[],[],[]]
-
-    result = mapReduce_kmeans(data,targetNB)
-
-    #mettre le premier 3 point comme le centres init
-    init_point_values = [result[i] for i in result.keys()][:3]
-    init_point_keys = [i for i in result.keys()][:3]
-
-    for key in centre.keys():
-        centre[key] = [1,init_point_values[key]]
-        cluster[key].append(init_point_keys[key])
-    
-
-    #init the centre new and result new for mapreduce
-    centre_new = {0:[0,0],1:[0,0],2:[0,0]}
-    result_new = dict()
-    #When the number of point of cluster don't change,stop
-    while True:
-        for eachkey in result:
-            if eachkey in cluster[0] or eachkey in cluster[1] or eachkey in cluster[2]:
-                continue
-            
-            #caculate the distance between the data of this lingne and the centre
-
-            #Map1_kemeans
-            result_new[eachkey] = map1_kmeans(result[eachkey])
-            centre_values = []
-            for each in centre:
-                centre_values.append(centre[each][1])
-            centre_values = mapCentre(centre_values)
-
-            #Reduce
-            result_new[eachkey] = reduceKmeans(result_new[eachkey],centre_values)
-
-            #Map2_kmeans
-            result_new[eachkey] = map2_kmeans(result_new[eachkey])
-        
-        #Put all the distance and points into the clusters
-        #Result format [cluster number, min(|tmpt - c|),tmpt - c]
-        for eachpoint in result_new:
-            clusterNB = result_new[eachpoint][0]
-            centre_new[clusterNB][0] += 1
-            centre_new[clusterNB][1] += result_new[eachpoint][2]
-            cluster[clusterNB].append(eachpoint)
-        
-        #compare centre_new and centre, if
-        if not cluster_nb_diff(centre_new,centre) > 1:
-            break
-        else:
-            #caculate the new centre
-            print ('jasdlkjalsdkjalskd         ',cluster_nb_diff(centre_new,centre))
-            for eachculster in centre_new:
-                centre_new[eachculster][1] = MapnewCentre(centre_new[eachculster])
-            centre = centre_new
-            centre_new = {0:[0,0],1:[0,0],2:[0,0]}
-            result_new = dict()
-            cluster = [[],[],[]]
-    createMap(cluster)
-
 
 def createMap(data):
     mean_lat = 0
@@ -179,27 +21,30 @@ def createMap(data):
     mean_lat = mean_lat/count
     mean_lon = mean_lon/count
 
+    m = folium.Map(location=[mean_lon,mean_lat], zoom_start=6)
 
-    m = folium.Map(location=[mean_lon,mean_lat],zoom_start=6)
-
-    color = {0:'blue',1:'red',2:'green'}
+    # Attributes names which will be displayed on the map
     attributes = ["alti", "drct", "dwpf", "feel", "gust", "ice_accretion_1hr", "ice_accretion_3hr", "ice_accretion_6hr",
                   "metar", "mslp", "p01i", "peak_wind_drct", "peak_wind_gust", "peak_wind_time", "relh", "sknt",
                   "skyc1", "skyc2", "skyc3", "skyc4", "skyl1", "skyl2", "skyl3", "skyl4", "tmpf", "vsby", "wxcodes"]
     for each in data.result():
         # print(each)
+        # Here we choose not to display the "nan" values and the METAR ID
         l = [attributes[i] + ":" + str(each[i + 4]) for i in range(len(attributes)) if str(each[i + 4]) != 'nan'
              and attributes[i] != "metar"]
         string='\n'.join(l)
         folium.Marker([each[2],each[1]],
-                    popup=string,
-                    icon=folium.Icon(color='red')).add_to(m)
+                      popup=string,
+                      icon=folium.Icon(color='red')).add_to(m)
     m.save("Projet-NF26/map.html")
 
 
-
 if __name__ == "__main__":
     session = connection()
-    timestamp = '2017-12-02 00:30:00'
+    # Timestamp user wants to search
+    date = input("Please enter the date you want to search (format 'yyyy-MM-dd'):")
+    time = input("Please enter the time you want to search (format 'hh:MM:ss'):")
+    #timestamp = '2017-12-02 00:30:00'
+    timestamp = date + ' ' + time
     data = session.execute_async("select * from meurouth_cql.database_time where date = '%s' ALLOW FILTERING"%(timestamp))
     createMap(data)