def process_args(api, args):
df = lpt.read_csv(args.filename)
date = lpt.to_date(args.date)
scope = args.scope
c1 = df.columns.values[0] # First column is the instrument
if args.fx:
scope = scope + "_FX"
df[c1] = "CCY_" + df[c1]
df["column3"] = None
else:
mi.map_instruments(api, df, c1)
# fix the column names
df.columns = ["instrument", "price", "ccy"]
def upsert_analytics(result=None):
analytics = [
api.models.InstrumentAnalytic(row["instrument"], row["price"],
row["ccy"])
for i, row in df.iterrows()
]
return api.call.set_analytics(scope, date.year, date.month, date.day,
analytics).bind(lambda r: None)
if args.update:
return upsert_analytics()
return api.call.create_analytic_store(
api.models.CreateAnalyticStoreRequest(scope,
date)).bind(upsert_analytics)
def process_args(api, args):
if args.folder:
args.input.extend([
os.path.join(args.folder, f) for f in os.listdir(args.folder)
if "-txn-" in f and f.endswith(".csv")
])
df = (pd.concat(
[lpt.read_csv(f)[[args.column]].drop_duplicates() for f in args.input],
ignore_index=True,
sort=True,
).drop_duplicates().reset_index(drop=True))
df.columns = ["FROM"]
df["TO"] = df["FROM"]
return map_instruments(api, df, "TO")
def include_mappings(path):
if path:
df = lpt.read_csv(path)
mapping_table.update(df.set_index("FROM")["TO"].to_dict())