added more codes CK time series& cutting

Haroldo_weigh_history_sel.py

@@ -0,0 +1,59 @@
+#! /usr/bin/env python
+
+import sys
+import os
+from database import *
+from datetime import *
+
+def main ():
+
+	database="calorie_king_social_networking_2010"  
+	server="tarraco.chem-eng.northwestern.edu"
+	user="calorieking" 
+	passwd="n1ckuDB!"
+
+	db=Connection(server, database, user, passwd) 
+
+	query="""Select `weigh_in_cuts`.`id` , `weigh_in_cuts`.`ck_id`, `weigh_in_cuts`.`fit_type`, `weigh_in_cuts`.`start_idx`, `weigh_in_cuts`.`stop_idx`,
+		`weigh_in_cuts`.`param1`,`weigh_in_cuts`.`param2`,`weigh_in_cuts`.`param3`
+		From `weigh_in_cuts`
+		Order by `weigh_in_cuts`.`ck_id`"""
+	selfile=db.query(query)
+
+
+
+
+	for row in selfile:
+		query="SELECT weigh_in_history.ck_id, weigh_in_history.on_day,weigh_in_history.weight FROM weigh_in_history WHERE weigh_in_history.ck_id='" + row['ck_id'] + "' Order by weigh_in_history.on_day"
+		res=db.query(query)
+
+
+		first_day=row['start_day']
+		last_day=row['stop_day']
+
+		fist_date=res[0]['on_date']
+		last_date=res[-1]['on_date']
+
+
+		count=0	
+		for his in res:
+
+
+			day=his['on_day']
+			if (day>=) and (count<row['stop_idx']):
+				print str(row['id']) + "\t" + str(his['ck_id']) + "\t"+ str(his['on_day']) + "\t" + str(his['weight']) + "\t" + str(row['fit_type']) + "\t" + str(row['param1']) + "\t" + str(row['param2']) + "\t" + str(row['param3'])
+			count=count+1
+
+	#weigh_join_friend=db.query(query)
+
+	#file=open("weigh_history.dat",'wt')
+
+			#for results in weigh_join_friend:
+			#ck_id=results['ck_id']
+			#on_day=results['on_day']
+			#weigh=results['weight']
+	#print >> file, ck_id + "\t" + str(on_day) +"\t" + str(weigh)
+
+
+if __name__== "__main__":
+	main()

analyze_frequency_gaps_in_time_series_frequencies.py

@@ -0,0 +1,220 @@
+
+#! /usr/bin/env python
+
+"""
+Created by Julia Poncela of October 2011
+
+Given a file for a non-stationary time serie, it calculates the optimum points to cut it, that mark different trends.
+
+More info: It follows the method proposed by Fukuda, Stanley and Amaral PRL 69, 2004.
+
+
+
+"""
+
+
+import sys
+import os
+from datetime import *
+import math
+import numpy as np
+from scipy import stats
+from database import *   #codigo para manejar bases de datos
+
+
+def main ():
+
+
+    top=50   #max: 8921  with filters  
+
+
+
+    database = "calorie_king_social_networking_2010"  
+    server="tarraco.chem-eng.northwestern.edu"
+    user="calorieking" 
+    passwd="n1ckuDB!"
+
+    db= Connection(server, database, user, passwd) 
+
+
+    db.execute ("DROP TABLE IF EXISTS gaps_by_frequency")  #i remove the (old) table 
+
+
+    db.execute ("""                      
+       CREATE TABLE gaps_by_frequency
+       (
+         file_index     INT,
+         ck_id          CHAR (20),
+         start_date     INT,
+         end_date       INT,
+         start_day      INT,
+         end_day        INT,
+         days_gap       INT,        
+         zscore_gap     FLOAT
+       )
+     """) # if i use triple quotation marks, i can have jumps of line no problem, but not with single ones 
+
+    #query="""describe gaps_by_frequency""" 
+    #db.execute ("DROP TABLE IF EXISTS animal")
+    # query="""show tables""" 
+
+
+
+
+
+    query="""select * from gaps_by_frequency""" 
+
+
+
+   # db.execute ("INSERT INTO gaps_by_frequency (file_index, ck_id, start_date, end_date, start_day, end_day, days_gap, std_freq, zscore_gap) VALUES (1, 'reptile',7, 4,1,20,18, 2.,3.) ")
+
+   # db.execute ("INSERT INTO gaps_by_frequency (file_index, ck_id, start_date, end_date, start_day, end_day, days_gap, std_freq, zscore_gap) VALUES ("+str(1)+", 'reptile',"+str(1)+", "+str(1)+","+str(1)+","+str(1)+","+str(1)+", "+str(1.)+","+str(1.)+") ")
+
+
+
+
+    #query="""show tables""" 
+
+
+ #   query="""select * from gaps_by_frequency""" 
+  #  result1 = db.query(query)  # is a list of dict.
+   # for r1 in result1:
+    #    print r1
+
+
+
+
+
+    list_all_average_frequencies=[]
+    histogram_all_freq_no_averaged=[0]*1000
+    num_events_all_freq_no_averaged=0.
+
+
+    for index_file in range(top):
+        index_file+=1
+
+        list_average_frequencies_one_user=[]
+        histogram_idiv=[0]*1000
+        num_events_indiv=0.
+
+
+#input file:      
+        file_name="temporal_series/most_weigh_ins/weigh_in_time_serie_days"+str(index_file)+"_top50"    
+        #file_name="temporal_series/most_weigh_ins/weigh_in_time_serie_days"+str(index_file)+"_filters"
+
+
+
+        file=open(file_name+".dat",'r')
+        list_lines_file=file.readlines()
+
+
+        list_dates=[]
+        list_days=[]
+        list_frequencies=[]
+        cont=0
+        for line in list_lines_file:
+            if cont>0:   # i skip the first line,cos it doesnt have an associated freq. 
+
+                list=line.split(" ")
+
+                ck_id=list[10]
+
+                print line
+                try:                               
+                    list_frequencies.append(float(list[9]))  #frequency                                                    
+                    list_days.append(float(list[4]))  #relative day    
+                    list_dates.append(list[7])  #dates    
+
+
+                except IndexError:                       
+
+                    list_frequencies.append(float(0.0)) #frequency                    
+                    list_days.append(float(list[4]))  #day                                      
+                    list_dates.append(list[7])  #dates    
+
+            cont+=1
+
+        print list_dates
+
+        print "\n\n"
+
+        list_zscores= stats.zs(list_frequencies)
+
+        for i in range(len(list_zscores)):
+
+            if list_zscores[i] >=3.0:  # statistically significant gap if zs>=3 std
+                if list_frequencies[i] >15.:# dont consider it a gap if it is shorter than 2weeks
+                    if  i>2:  #or happens for the very second measurement
+
+                        print "on file",index_file,"between days:",list_days[i-1],"-",list_days[i], "there is a gap. freq:", list_frequencies[i],"zscore:",list_zscores[i]
+
+                        time_gap=list_days[i]-list_days[i-1]
+
+                        db.execute ("INSERT INTO gaps_by_frequency (file_index, ck_id, start_date, end_date, start_day, end_day, days_gap, zscore_gap) VALUES ("+str(index_file)+", "+str(ck_id)+","+str(list_dates[i-1])+", "+str(list_dates[i])+","+str(list_days[i-1])+","+str(list_days[i])+","+str(time_gap)+", "+str(list_zscores[i])+" ")
+
+
+
+            print "\n","on file",index_file,"mean freq:",np.asanyarray(list_frequencies).mean(axis=0),"std:",np.asanyarray(list_frequencies).std(axis=0, ddof=0)
+
+
+
+
+
+
+
+
+        raw_input()
+
+
+##################################
+
+def zscore(a, axis=0, ddof=0):
+	    """
+	    Calculates the z score of each value in the sample, relative to the sample
+	    mean and standard deviation. 
+	 
+	    Parameters
+	    ----------
+	    a: array_like
+	       An array like object containing the sample data
+	    axis: int or None, optional
+	         If axis is equal to None, the array is first ravel'd. If axis is an
+	         integer, this is the axis over which to operate. Defaults to 0.
+	   
+
+            ddof : int, optional
+                 Degrees of freedom correction in the calculation 
+                 of the standard deviation. Default is 0.
+
+	    Returns
+	    -------
+	    zscore: array_like
+	        the z-scores, standardized by mean and standard deviation of input
+	        array
+	
+	    Notes
+	    -----
+	    This function does not convert array classes, and works also with
+	    matrices and masked arrays.
+	
+	    """
+	    a = np.asanyarray(a)
+	    mns = a.mean(axis=axis)
+	    sstd = a.std(axis=axis, ddof=ddof)
+	    if axis and mns.ndim < a.ndim:
+	        return ((a - np.expand_dims(mns, axis=axis) /
+	                 np.expand_dims(sstd,axis=axis)))
+	    else:
+	        return (a - mns) / sstd
+
+
+
+#########################
+
+if __name__== "__main__":
+
+    main()
+
+
+
+##############################################