WIP: Transforms
Create an interface where users can easily transform SQL returns from simple Lists to other datastructures.
import pyticdb
result = pyticdb.query.query_by_loc(30, 30, 5, "id", "tmag", "ra", "dec")
df = result.to(pd.DataFrame, columns=["id", "tmag", "ra", "dec"])
print(df)
# id tmag ra dec
#0 620522458 19.5013 26.517966 26.067170
#1 26854028 16.9362 26.554431 26.058153
#2 238602786 13.7684 25.831204 26.607332
#3 26780500 16.6801 25.749914 26.705586
#4 26780499 17.3535 25.751117 26.706271
#... ... ... ... ...
#465131 63673024 15.4218 33.113164 34.242920
#465132 620976192 18.9674 33.113467 34.242985
#465133 620976196 19.3668 33.100306 34.253915
#465134 620976198 19.5535 33.082656 34.263246
#465135 620976186 19.2301 33.122409 34.237523
#[465136 rows x 4 columns]Additionally mapped collections may also be generated
result = pyticdb.query.query_by_loc(30, 30, 5, "id", "tmag", "ra", "dec")
mapping = result.to_mapping()
print(mapping)
#{
#...
# 620586895: (620586895, 18.2195, 25.9165267339, 26.7210167303),
# 26821818: (26821818, 17.4758, 25.9313564236, 26.7138267236),
# 26821816: (26821816, 17.4579, 25.9229220787, 26.7281658026),
# 620586896: (620586896, 19.5342, 25.9287999639, 26.7293418159),
# 238602753: (238602753, 17.287, 25.898837, 26.739758),
# 620586901: (620586901, 17.599, 25.9118859569, 26.7430910162),
#...
#}
print(mapping[620586901].tmag)
# 17.599