mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-14 08:16:54 +08:00
fix CI error
This commit is contained in:
@@ -324,7 +324,6 @@ class TRAModel(Model):
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class LSTM(nn.Module):
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class LSTM(nn.Module):
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"""LSTM Model
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"""LSTM Model
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Args:
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Args:
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@@ -414,7 +413,6 @@ class PositionalEncoding(nn.Module):
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class Transformer(nn.Module):
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class Transformer(nn.Module):
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"""Transformer Model
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"""Transformer Model
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Args:
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Args:
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@@ -475,7 +473,6 @@ class Transformer(nn.Module):
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class TRA(nn.Module):
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class TRA(nn.Module):
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"""Temporal Routing Adaptor (TRA)
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"""Temporal Routing Adaptor (TRA)
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TRA takes historical prediction errors & latent representation as inputs,
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TRA takes historical prediction errors & latent representation as inputs,
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@@ -162,13 +162,15 @@ def create_account_instance(
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init_cash=init_cash,
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init_cash=init_cash,
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position_dict=position_dict,
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position_dict=position_dict,
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pos_type=pos_type,
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pos_type=pos_type,
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benchmark_config={}
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benchmark_config=(
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if benchmark is None
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{}
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else {
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if benchmark is None
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"benchmark": benchmark,
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else {
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"start_time": start_time,
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"benchmark": benchmark,
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"end_time": end_time,
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"start_time": start_time,
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},
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"end_time": end_time,
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}
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),
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)
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)
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@@ -622,9 +622,11 @@ class Indicator:
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print(
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print(
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"[Indicator({}) {}]: FFR: {}, PA: {}, POS: {}".format(
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"[Indicator({}) {}]: FFR: {}, PA: {}, POS: {}".format(
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freq,
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freq,
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trade_start_time
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(
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if isinstance(trade_start_time, str)
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trade_start_time
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else trade_start_time.strftime("%Y-%m-%d %H:%M:%S"),
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if isinstance(trade_start_time, str)
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else trade_start_time.strftime("%Y-%m-%d %H:%M:%S")
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),
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fulfill_rate,
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fulfill_rate,
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price_advantage,
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price_advantage,
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positive_rate,
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positive_rate,
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@@ -3,6 +3,7 @@ Here is a batch of evaluation functions.
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The interface should be redesigned carefully in the future.
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The interface should be redesigned carefully in the future.
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"""
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"""
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import pandas as pd
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import pandas as pd
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from typing import Tuple
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from typing import Tuple
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from qlib import get_module_logger
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from qlib import get_module_logger
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@@ -511,7 +511,6 @@ class TRAModel(Model):
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class RNN(nn.Module):
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class RNN(nn.Module):
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"""RNN Model
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"""RNN Model
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Args:
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Args:
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@@ -601,7 +600,6 @@ class PositionalEncoding(nn.Module):
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class Transformer(nn.Module):
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class Transformer(nn.Module):
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"""Transformer Model
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"""Transformer Model
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Args:
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Args:
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@@ -649,7 +647,6 @@ class Transformer(nn.Module):
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class TRA(nn.Module):
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class TRA(nn.Module):
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"""Temporal Routing Adaptor (TRA)
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"""Temporal Routing Adaptor (TRA)
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TRA takes historical prediction errors & latent representation as inputs,
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TRA takes historical prediction errors & latent representation as inputs,
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@@ -373,7 +373,6 @@ class WeightStrategyBase(BaseSignalStrategy):
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class EnhancedIndexingStrategy(WeightStrategyBase):
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class EnhancedIndexingStrategy(WeightStrategyBase):
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"""Enhanced Indexing Strategy
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"""Enhanced Indexing Strategy
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Enhanced indexing combines the arts of active management and passive management,
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Enhanced indexing combines the arts of active management and passive management,
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@@ -71,15 +71,11 @@ def fetch_df_by_index(
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if fetch_orig:
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if fetch_orig:
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for slc in idx_slc:
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for slc in idx_slc:
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if slc != slice(None, None):
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if slc != slice(None, None):
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return df.loc[
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return df.loc[pd.IndexSlice[idx_slc],] # noqa: E231
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pd.IndexSlice[idx_slc],
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] # noqa: E231
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else: # pylint: disable=W0120
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else: # pylint: disable=W0120
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return df
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return df
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else:
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else:
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return df.loc[
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return df.loc[pd.IndexSlice[idx_slc],] # noqa: E231
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pd.IndexSlice[idx_slc],
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] # noqa: E231
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def fetch_df_by_col(df: pd.DataFrame, col_set: Union[str, List[str]]) -> pd.DataFrame:
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def fetch_df_by_col(df: pd.DataFrame, col_set: Union[str, List[str]]) -> pd.DataFrame:
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@@ -30,7 +30,6 @@ class Ensemble:
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class SingleKeyEnsemble(Ensemble):
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class SingleKeyEnsemble(Ensemble):
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"""
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"""
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Extract the object if there is only one key and value in the dict. Make the result more readable.
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Extract the object if there is only one key and value in the dict. Make the result more readable.
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{Only key: Only value} -> Only value
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{Only key: Only value} -> Only value
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@@ -64,7 +63,6 @@ class SingleKeyEnsemble(Ensemble):
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class RollingEnsemble(Ensemble):
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class RollingEnsemble(Ensemble):
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"""Merge a dict of rolling dataframe like `prediction` or `IC` into an ensemble.
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"""Merge a dict of rolling dataframe like `prediction` or `IC` into an ensemble.
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NOTE: The values of dict must be pd.DataFrame, and have the index "datetime".
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NOTE: The values of dict must be pd.DataFrame, and have the index "datetime".
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@@ -247,9 +247,7 @@ class ShrinkCovEstimator(RiskModel):
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v1 = y.T.dot(z) / t - cov_mkt[:, None] * S
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v1 = y.T.dot(z) / t - cov_mkt[:, None] * S
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roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt
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roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt
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v3 = z.T.dot(z) / t - var_mkt * S
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v3 = z.T.dot(z) / t - var_mkt * S
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roff3 = (
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roff3 = np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
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np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
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)
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roff = 2 * roff1 - roff3
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roff = 2 * roff1 - roff3
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rho = rdiag + roff
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rho = rdiag + roff
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@@ -90,7 +90,6 @@ class OnlineStrategy:
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class RollingStrategy(OnlineStrategy):
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class RollingStrategy(OnlineStrategy):
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"""
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"""
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This example strategy always uses the latest rolling model sas online models.
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This example strategy always uses the latest rolling model sas online models.
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"""
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"""
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@@ -146,9 +146,7 @@ class DumpDataBase:
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return (
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return (
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self._include_fields
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self._include_fields
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if self._include_fields
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if self._include_fields
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else set(df_columns) - set(self._exclude_fields)
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else set(df_columns) - set(self._exclude_fields) if self._exclude_fields else df_columns
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if self._exclude_fields
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else df_columns
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)
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)
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@staticmethod
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@staticmethod
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@@ -132,9 +132,11 @@ class DumpPitData:
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return (
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return (
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set(self._include_fields)
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set(self._include_fields)
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if self._include_fields
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if self._include_fields
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else set(df[self.field_column_name]) - set(self._exclude_fields)
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else (
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if self._exclude_fields
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set(df[self.field_column_name]) - set(self._exclude_fields)
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else set(df[self.field_column_name])
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if self._exclude_fields
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else set(df[self.field_column_name])
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)
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)
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)
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def get_filenames(self, symbol, field, interval):
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def get_filenames(self, symbol, field, interval):
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