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csvloader.py
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import csv
from collections import namedtuple
def clean_value(val):
if val is None:
return None
try:
return int(val)
except ValueError:
return val.replace("\\n", "\n")
# Load a database file (equivalent of treasurebox arks)
# For runtime-computed parameters, pass a function into kwargs
# for the attribute name you want, the function will be called
# with the loaded object, and the result added under the kwarg name.
# This is particularly useful for joining multiple DBs with a
# foreign-key relationship.
# Ex. >>> data = load_db_file("some.csv", myattr=lambda raw: raw.id + 1)
# >>> print(data[0])
# (id=0, myattr=1)
def load_db_file(file, **kwargs):
class_name = file.split("/")[-1].rsplit(".", 1)[0] + "_t"
with open(file, "r") as cin:
reader = csv.reader(cin)
fields = next(reader)
raw_field_len = len(fields)
# print(fields)
the_raw_type = namedtuple("_" + class_name, fields)
keys = list(kwargs.keys())
for key in keys:
fields.append(key)
the_type = namedtuple(class_name, fields)
for val_list in filter(lambda list: len(list) == raw_field_len, reader):
temp_obj = the_raw_type(*map(clean_value, val_list))
try:
extvalues = tuple(kwargs[key](temp_obj) for key in keys)
except Exception:
raise RuntimeError(
"Uncaught exception while filling stage2 data for {0}. Are you missing data?".format(temp_obj))
yield the_type(*temp_obj + extvalues)
# Load a database file, and return a dict keyed by the content of the
# key_col parameter. This performs the same runtime-computing as
# load_db_file.
# Ex. >>> data = load_keyed_db_file("some.csv")
# >>> print(data[0])
# (id=13, another_column="string")
# >>> print(data)
# {13: (id=13, another_column="string")}
def load_keyed_db_file(file, key_col=0, **kwargs):
ret_dic = {}
tab = load_db_file(file, **kwargs)
for thing in tab:
ret_dic[thing[0]] = thing
return ret_dic
if __name__ == "__main__":
import sys
want_keys = sys.argv[2:]
file = sys.argv[1]
for entry in load_db_file(file):
print(*(getattr(entry, key) for key in want_keys))