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import numpy as np |
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import pandas as pd |
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import typing |
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from .abstract_importer import AbstractImporter |
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class SampleImporter(AbstractImporter): |
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"""Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare |
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the data loaded directly by using DataFrame |
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:param trajectory_list: the data that describes the trajectories |
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:type trajectory_list: typing.Union[pd.DataFrame, np.ndarray, typing.List] |
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:param variables: the data that describes the variables with name and cardinality |
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:type variables: typing.Union[pd.DataFrame, np.ndarray, typing.List] |
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:param prior_net_structure: the data of the real structure, if it exists |
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:type prior_net_structure: typing.Union[pd.DataFrame, np.ndarray, typing.List] |
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:_df_samples_list: a Dataframe list in which every dataframe contains a trajectory |
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:_raw_data: The raw contents of the json file to import |
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:type _raw_data: List |
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""" |
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def __init__(self, |
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trajectory_list: typing.Union[typing.List, np.ndarray] = None, |
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variables: typing.Union[pd.DataFrame, np.ndarray, typing.List] = None, |
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prior_net_structure: typing.Union[pd.DataFrame, np.ndarray,typing.List] = None): |
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'If the data are not DataFrame, it will be converted' |
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if isinstance(variables,list) or isinstance(variables,np.ndarray): |
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variables = pd.DataFrame(variables) |
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if isinstance(variables,list) or isinstance(variables,np.ndarray): |
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prior_net_structure=pd.DataFrame(prior_net_structure) |
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super(SampleImporter, self).__init__(file_path=None, trajectories_list =trajectory_list, |
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variables= variables, |
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prior_net_structure=prior_net_structure) |
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def import_data(self, header_column = None): |
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if header_column is not None: |
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self._sorter = header_column |
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else: |
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self._sorter = self.build_sorter(self._df_samples_list[0]) |
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samples_list= self._df_samples_list |
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if isinstance(samples_list, np.ndarray): |
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samples_list = samples_list.tolist() |
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self.compute_row_delta_in_all_samples_frames(samples_list) |
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def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: |
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"""Implements the abstract method build_sorter of the :class:`AbstractImporter` in order to get the ordered variables list. |
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""" |
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columns_header = list(sample_frame.columns.values) |
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del columns_header[0] |
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return columns_header |
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def dataset_id(self) -> object: |
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pass |
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