Analyzing distances between clusters of orders











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I wrote the python class below which does what I want it to do but the data structure is a mess. Was wondering if there was a better structure I could use to get the same results but also have the code be much more readable.



idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs



class OrderProximityModel:
def __init__(self, date):
self.date = str(date)
self.order_data = OrderProxDao().Load_Order_Lat_Long_Date_Zone_Data(self.date)
self.distinct = set([str(row.Requirements) + ' ' + str(row.Route_Date) for row in self.order_data])


def organizer(self):
container =
for date_zone in self.distinct:
latlng = list(filter(lambda x: str(x.Requirements) + ' ' + str(x.Route_Date) == date_zone, self.order_data))
for i in self.map_loc_to_lat_long(latlng):
container.append((i[0][0][0], i[0][0][1], i[0][0][2], i[0][0][4], i[0][0][5], i[0][0][6]))
InsertHelpers(container).chunk_maker(100)
return True


def map_loc_to_lat_long(self, grouped_zone):
converted = {}
for row in grouped_zone:
converted[row.LocationKey] = [row.Latitude, row.Longitude, row.DA, row.Route_Date, row.Requirements, row.DA]
grouped_combos = self.combo_creator(converted.keys())
return map(lambda combo: ([converted[combo[0]][2:] + [combo[0]] + [combo[1]] +
[StraightLineDistance().dist_cal(converted[combo[0]][0],
converted[combo[0]][1],
converted[combo[1]][0],
converted[combo[1]][1])]],
), grouped_combos)

@staticmethod
def combo_creator(inputs):
out =
for index, value in enumerate(inputs):
for nex_value in inputs[index + 1:]:
out.append((value, nex_value))
return out









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  • It "does what I want it to do" — would you care to tell us what that is, exactly? See How to Ask.
    – 200_success
    7 hours ago










  • Sure, idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs
    – Mike Sivalls
    7 hours ago










  • Absolutely, thanks
    – Mike Sivalls
    1 hour ago















up vote
0
down vote

favorite












I wrote the python class below which does what I want it to do but the data structure is a mess. Was wondering if there was a better structure I could use to get the same results but also have the code be much more readable.



idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs



class OrderProximityModel:
def __init__(self, date):
self.date = str(date)
self.order_data = OrderProxDao().Load_Order_Lat_Long_Date_Zone_Data(self.date)
self.distinct = set([str(row.Requirements) + ' ' + str(row.Route_Date) for row in self.order_data])


def organizer(self):
container =
for date_zone in self.distinct:
latlng = list(filter(lambda x: str(x.Requirements) + ' ' + str(x.Route_Date) == date_zone, self.order_data))
for i in self.map_loc_to_lat_long(latlng):
container.append((i[0][0][0], i[0][0][1], i[0][0][2], i[0][0][4], i[0][0][5], i[0][0][6]))
InsertHelpers(container).chunk_maker(100)
return True


def map_loc_to_lat_long(self, grouped_zone):
converted = {}
for row in grouped_zone:
converted[row.LocationKey] = [row.Latitude, row.Longitude, row.DA, row.Route_Date, row.Requirements, row.DA]
grouped_combos = self.combo_creator(converted.keys())
return map(lambda combo: ([converted[combo[0]][2:] + [combo[0]] + [combo[1]] +
[StraightLineDistance().dist_cal(converted[combo[0]][0],
converted[combo[0]][1],
converted[combo[1]][0],
converted[combo[1]][1])]],
), grouped_combos)

@staticmethod
def combo_creator(inputs):
out =
for index, value in enumerate(inputs):
for nex_value in inputs[index + 1:]:
out.append((value, nex_value))
return out









share|improve this question









New contributor




Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




















  • It "does what I want it to do" — would you care to tell us what that is, exactly? See How to Ask.
    – 200_success
    7 hours ago










  • Sure, idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs
    – Mike Sivalls
    7 hours ago










  • Absolutely, thanks
    – Mike Sivalls
    1 hour ago













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I wrote the python class below which does what I want it to do but the data structure is a mess. Was wondering if there was a better structure I could use to get the same results but also have the code be much more readable.



idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs



class OrderProximityModel:
def __init__(self, date):
self.date = str(date)
self.order_data = OrderProxDao().Load_Order_Lat_Long_Date_Zone_Data(self.date)
self.distinct = set([str(row.Requirements) + ' ' + str(row.Route_Date) for row in self.order_data])


def organizer(self):
container =
for date_zone in self.distinct:
latlng = list(filter(lambda x: str(x.Requirements) + ' ' + str(x.Route_Date) == date_zone, self.order_data))
for i in self.map_loc_to_lat_long(latlng):
container.append((i[0][0][0], i[0][0][1], i[0][0][2], i[0][0][4], i[0][0][5], i[0][0][6]))
InsertHelpers(container).chunk_maker(100)
return True


def map_loc_to_lat_long(self, grouped_zone):
converted = {}
for row in grouped_zone:
converted[row.LocationKey] = [row.Latitude, row.Longitude, row.DA, row.Route_Date, row.Requirements, row.DA]
grouped_combos = self.combo_creator(converted.keys())
return map(lambda combo: ([converted[combo[0]][2:] + [combo[0]] + [combo[1]] +
[StraightLineDistance().dist_cal(converted[combo[0]][0],
converted[combo[0]][1],
converted[combo[1]][0],
converted[combo[1]][1])]],
), grouped_combos)

@staticmethod
def combo_creator(inputs):
out =
for index, value in enumerate(inputs):
for nex_value in inputs[index + 1:]:
out.append((value, nex_value))
return out









share|improve this question









New contributor




Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











I wrote the python class below which does what I want it to do but the data structure is a mess. Was wondering if there was a better structure I could use to get the same results but also have the code be much more readable.



idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs



class OrderProximityModel:
def __init__(self, date):
self.date = str(date)
self.order_data = OrderProxDao().Load_Order_Lat_Long_Date_Zone_Data(self.date)
self.distinct = set([str(row.Requirements) + ' ' + str(row.Route_Date) for row in self.order_data])


def organizer(self):
container =
for date_zone in self.distinct:
latlng = list(filter(lambda x: str(x.Requirements) + ' ' + str(x.Route_Date) == date_zone, self.order_data))
for i in self.map_loc_to_lat_long(latlng):
container.append((i[0][0][0], i[0][0][1], i[0][0][2], i[0][0][4], i[0][0][5], i[0][0][6]))
InsertHelpers(container).chunk_maker(100)
return True


def map_loc_to_lat_long(self, grouped_zone):
converted = {}
for row in grouped_zone:
converted[row.LocationKey] = [row.Latitude, row.Longitude, row.DA, row.Route_Date, row.Requirements, row.DA]
grouped_combos = self.combo_creator(converted.keys())
return map(lambda combo: ([converted[combo[0]][2:] + [combo[0]] + [combo[1]] +
[StraightLineDistance().dist_cal(converted[combo[0]][0],
converted[combo[0]][1],
converted[combo[1]][0],
converted[combo[1]][1])]],
), grouped_combos)

@staticmethod
def combo_creator(inputs):
out =
for index, value in enumerate(inputs):
for nex_value in inputs[index + 1:]:
out.append((value, nex_value))
return out






python python-2.x clustering geospatial






share|improve this question









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Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|improve this question









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Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|improve this question




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edited 5 mins ago









200_success

127k15148411




127k15148411






New contributor




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Check out our Code of Conduct.









asked 8 hours ago









Mike Sivalls

61




61




New contributor




Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.





New contributor





Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






Mike Sivalls is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.












  • It "does what I want it to do" — would you care to tell us what that is, exactly? See How to Ask.
    – 200_success
    7 hours ago










  • Sure, idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs
    – Mike Sivalls
    7 hours ago










  • Absolutely, thanks
    – Mike Sivalls
    1 hour ago


















  • It "does what I want it to do" — would you care to tell us what that is, exactly? See How to Ask.
    – 200_success
    7 hours ago










  • Sure, idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs
    – Mike Sivalls
    7 hours ago










  • Absolutely, thanks
    – Mike Sivalls
    1 hour ago
















It "does what I want it to do" — would you care to tell us what that is, exactly? See How to Ask.
– 200_success
7 hours ago




It "does what I want it to do" — would you care to tell us what that is, exactly? See How to Ask.
– 200_success
7 hours ago












Sure, idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs
– Mike Sivalls
7 hours ago




Sure, idea here is we retrieve a dataset from SQL(constructor), cluster the dataset into distinct keys(constructor), iterate through the keys and isolate the matching criteria in the dataset(organizer), pass those data chunks to map_loc_to_lat_long which will find all possible combinations of rows in the chunk and find the straight line distance between all the combination Lat Longs
– Mike Sivalls
7 hours ago












Absolutely, thanks
– Mike Sivalls
1 hour ago




Absolutely, thanks
– Mike Sivalls
1 hour ago















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