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provide dtype to empty series to surpress warning; fix type (#1449)
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@@ -783,7 +783,7 @@ class LocalPITProvider(PITProvider):
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index_path = C.dpm.get_data_uri() / "financial" / instrument.lower() / f"{field}.index"
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data_path = C.dpm.get_data_uri() / "financial" / instrument.lower() / f"{field}.data"
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if not (index_path.exists() and data_path.exists()):
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raise FileNotFoundError("No file is found. Raise exception and ")
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raise FileNotFoundError("No file is found.")
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# NOTE: The most significant performance loss is here.
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# Does the acceleration that makes the program complicated really matters?
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# - It makes parameters of the interface complicate
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@@ -797,14 +797,14 @@ class LocalPITProvider(PITProvider):
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cur_time_int = int(cur_time.year) * 10000 + int(cur_time.month) * 100 + int(cur_time.day)
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loc = np.searchsorted(data["date"], cur_time_int, side="right")
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if loc <= 0:
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return pd.Series()
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return pd.Series(dtype=C.pit_record_type["value"])
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last_period = data["period"][:loc].max() # return the latest quarter
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first_period = data["period"][:loc].min()
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period_list = get_period_list(first_period, last_period, quarterly)
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if period is not None:
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# NOTE: `period` has higher priority than `start_index` & `end_index`
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if period not in period_list:
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return pd.Series()
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return pd.Series(dtype=C.pit_record_type["value"])
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else:
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period_list = [period]
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else:
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@@ -868,7 +868,7 @@ class LocalExpressionProvider(ExpressionProvider):
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# Ensure that each column type is consistent
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# FIXME:
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# 1) The stock data is currently float. If there is other types of data, this part needs to be re-implemented.
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# 2) The the precision should be configurable
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# 2) The precision should be configurable
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try:
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series = series.astype(np.float32)
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except ValueError:
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