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synced 2025-06-26 19:41:12 +02:00
Fix bug. Saved flags from apps were not associated with right parent variable.
This commit is contained in:
File diff suppressed because it is too large
Load Diff
@ -1,301 +1,301 @@
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import dima.src.hdf5_ops as h5de
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from plotly.subplots import make_subplots
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import plotly.graph_objs as go
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import base64
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import os
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import numpy as np
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import pandas as pd
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import dima.utils.g5505_utils as utils
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UPLOAD_DIRECTORY = 'data_products/'
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flags_dict = {
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"000" : {"flag_label": 'V', "flag_description": "Valid measurement"},
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"100" : {"flag_label": 'V', "flag_description": "Checked by data originator. Valid measurement, overrides any invalid flags"},
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"110" : {"flag_label": 'V', "flag_description": "Episode data checked and accepted by data originator. Valid measurement"},
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"111" : {"flag_label": 'V', "flag_description": "Irregular data checked and accepted by data originator. Valid measurement"},
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"456" : {"flag_label": 'I', "flag_description": "Invalidated by data originator"},
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"460" : {"flag_label": 'I', "flag_description": "Contamination suspected"},
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"559" : {"flag_label": 'V', "flag_description": "Unspecified contamination or local influence, but considered valid"},
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"599" : {"flag_label": 'I', "flag_description": "Unspecified contamination or local influence"},
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"652" : {"flag_label": 'V', "flag_description": "construction/activity nearby"},
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"659" : {"flag_label": 'I', "flag_description": "Unspecified instrument/sampling anomaly"},
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"660" : {"flag_label": 'V', "flag_description": "Unspecified instrument/sampling anomaly"},
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"999" : {"flag_label": 'I', "flag_description": "Missing measurement, unspecified reason"}
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}
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def save_file(name, content):
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# Decode the content and save the file
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content_type, content_string = content.split(',')
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decoded = base64.b64decode(content_string)
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file_path = os.path.join(UPLOAD_DIRECTORY, name)
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if not os.path.exists(file_path):
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with open(file_path, "wb") as f:
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f.write(decoded)
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print(f"File saved successfully at {file_path}")
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return file_path
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else:
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print(f'File already exists at {file_path}.\nTo maintain the integrity of the existing file, it will not be overwritten.')
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return file_path
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def filter_flags_by_label(flags_dict, label):
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"""
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Filters the flags dictionary by the specified label.
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Parameters:
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-----------
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flags_dict (dict): The dictionary containing flags.
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label (str): The label to filter by ('I' or 'V').
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Returns:
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--------
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list: A list of dictionaries with 'label' and 'value' for the specified label.
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"""
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return [{'label': value['flag_description'], 'value': code}
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for code, value in flags_dict.items() if value['flag_label'] == label]
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def create_loaded_file_figure(file_path, instFolder, dataset_name, datetime_var, datetime_var_format, variables):
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DataOpsAPI = h5de.HDF5DataOpsManager(file_path)
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if not DataOpsAPI.file_obj:
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DataOpsAPI.load_file_obj()
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#target_channels = DataOpsAPI.file_obj[instfolder].attrs['target_channels']['names'][0].decode().split(',')
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#target_loc = DataOpsAPI.file_obj[instfolder].attrs['target_channels']['location'][0].decode()
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#diagnostic_channels = DataOpsAPI.file_obj[instfolder].attrs['diagnostic_channels']['names'][0].decode().split(',')
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#diagnostic_loc = DataOpsAPI.file_obj[instfolder].attrs['diagnostic_channels']['location'][0].decode()
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#fig = make_subplots(rows=(len(target_channels+diagnostic_channels)-2), cols=1, shared_xaxes=True,
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# row_heights = [1 for i in range(len(target_channels+diagnostic_channels)-2)])
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fig = make_subplots(rows=(len(variables)), cols=1,
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row_heights = [1 for i in range(len(variables))])
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traces = []
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trace_idx = 1
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dataset = DataOpsAPI.file_obj[dataset_name]
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time_column = DataOpsAPI.reformat_datetime_column(dataset_name,
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datetime_var,
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datetime_var_format)
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#time_column = dataset[datetime_var][:]
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for i in range(1,len(variables)):
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fig.add_trace(go.Scatter(x = time_column,
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y = dataset[variables[i]][:],
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mode = 'lines',
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name = variables[i]), row=trace_idx, col=1)
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fig.update_yaxes(title_text= variables[i], row=trace_idx, col=1)
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trace_idx = trace_idx + 1
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#dataset = DataOpsAPI.file_obj[diagnostic_loc]
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#time_column = DataOpsAPI.reformat_datetime_column(diagnostic_loc,diagnostic_channels[0],'%d.%m.%Y %H:%M:%S')
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#for i in range(1,len(diagnostic_channels)):
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# fig.add_trace(go.Scatter(x = time_column,
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# y = dataset[diagnostic_channels[i]][:],
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# mode = 'lines',
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# name = diagnostic_channels[i]), row=trace_idx, col=1)
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# fig.update_yaxes(title_text= diagnostic_channels[i], row=trace_idx, col=1, type="log")
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# trace_idx = trace_idx + 1
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fig.update_layout(height=1200, title_text=f"{instFolder} : Target and Diagnostic Channels", showlegend=False)
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DataOpsAPI.unload_file_obj()
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#target_channels.remove(target_channels[0])
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#diagnostic_channels.remove(diagnostic_channels[0])
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return fig , [','.join([item,dataset_name]) for item in variables] #+ [','.join([item,diagnostic_loc]) for item in diagnostic_channels]
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#import os
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import json
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import h5py
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def load_flags(flagFolderPath, dry_run : bool = False): #filePath, instFolder, dry_run : bool = False):
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"""
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Returns a list of flags (dictionaries) based on the provided filePath and instFolder.
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Parameters:
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-----------
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filePath (str): The path to the uploaded file, expected to have an .h5 extension.
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instFolder (str): The name of the instrument folder, which must exist as a group in the HDF5 file.
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dry_run (bool): If True, performs all operations except loading file contents.
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Returns:
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--------
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list: A list of dictionaries containing flag data (or file paths in dry_run mode),
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or None if conditions are not met.
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"""
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# Return None if the flags folder does not exist
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if not os.path.exists(flagFolderPath):
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return None
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# List files in the flags folder
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files = [os.path.join(flagFolderPath, f) for f in os.listdir(flagFolderPath)]
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# If no files found, return None
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if not files:
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return None
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# Sort files by creation time
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sortedFiles = sorted(files, key=os.path.getctime)
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if dry_run:
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print(f"Dry run: Found {len(sortedFiles)} files in the flags folder:")
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for filePath in sortedFiles:
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print(f" - {filePath}")
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return sortedFiles # Return file paths in dry run mode
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# Process and load JSON files
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flagDataList = []
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for filePath in sortedFiles:
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if filePath.endswith('.json'):
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try:
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with open(filePath, 'r') as file:
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flagDataList.append(json.load(file))
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except (json.JSONDecodeError, FileNotFoundError) as e:
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print(f"Error loading file {filePath}: {e}")
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continue # Skip invalid or missing files
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return flagDataList
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class FlaggingAppDataManager():
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def __init__(self, file_path, mode = 'r+') -> None:
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self.file_path = file_path
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self.mode = mode
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self._data_ops_obj = None
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self.file_obj = None
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self.datasets_metadata_df = None
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return None
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def load_file_obj(self):
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self._data_ops_obj = h5de.HDF5DataOpsManager(self.file_path, self.mode)
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self._data_ops_obj.load_file_obj()
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self.file_obj = self._data_ops_obj.file_obj
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def unload_file_obj(self):
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self._data_ops_obj = h5de.HDF5DataOpsManager(self.file_path, self.mode)
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self._data_ops_obj.unload_file_obj() # sets __data_ops_obj.file_obj to None
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def transfer_flags(self):
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if self.file_obj is None:
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raise RuntimeError("File object is not loaded. Please load the HDF5 file using the 'load_file_obj' method before attempting to modify it.")
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path_to_append_dir, ext = os.path.splitext(self.file_path)
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self._data_ops_obj.update_file(path_to_append_dir)
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def apply_flags(self,instFolder):
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# TODO: apply flags to diagnostic and indivial channels. so far is all channels are cleaned
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if self.file_obj is None:
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raise RuntimeError("File object is not loaded. Please load the HDF5 file using the 'load_file_obj' method before attempting to modify it.")
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DataOpsManager = self._data_ops_obj
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file_obj = self.file_obj
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#with h5py.File(self.file_path, mode = self.mode, track_order=True) as file_obj:
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try:
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if not instFolder in file_obj:
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raise ValueError(f'Invalid instrument name. Instrument folder {instFolder} was not found in file {self.file_path}.')
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if '_'.join([instFolder,'flags']) not in flag_obj:
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raise RuntimeWarning(f'There is no flags to apply. ')
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if not ('diagnostic_channels' in file_obj[instFolder].attrs and 'target_channels' in file_obj[instFolder].attrs):
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raise ValueError(
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f'Required attributes missing. Instrument folder {instFolder} in file {self.file_path} has to be annotated with '
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'attributes "diagnostic_channels" and "target_channels" that specify channels location and their names.'
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)
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dataset_name = file_obj[instFolder].attrs['target_channels']['location'][0].decode()
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channel_names = file_obj[instFolder].attrs['target_channels']['names'][0].decode().split(',')
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dataset_obj = file_obj[dataset_name]
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# TODO: maybe we can do this directly on dataset = dataset_obj[...], which is a structured numpy array, instead of wrapping that as dataframe
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dataset_df = DataOpsManager.extract_dataset_as_dataframe(dataset_name)
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# Define datetime variable based on channel names. We assume by design the first entry of the list is the datetime variable name.
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datetime_var = channel_names[0]
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remaining_vars = channel_names.copy()
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remaining_vars.remove(datetime_var)
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ref_datetime_format = dataset_obj.attrs.get(datetime_var,None)['datetime_format'][0].decode()
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#datetime_var_data = pd.Series([item.decode() for item in dataset_obj[datetime_var]])
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#datetime_var_data = pd.to_datetime(datetime_var_data , format = ref_datetime_format, errors = 'coerce')
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dataset_df[datetime_var] = dataset_df[datetime_var].apply(lambda x: x.decode() )
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dataset_df[datetime_var] = pd.to_datetime(dataset_df[datetime_var], format = ref_datetime_format, errors = 'coerce')
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flag_indicator = np.zeros(shape = dataset_df[datetime_var].shape,
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dtype = bool)
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# TODO: include this information as part of the flag's attributes in the flag recording process
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flag_datetime_format='%Y-%m-%d %H:%M:%S.%f'
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for flag in file_obj[f'{instFolder}_flags']:
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flag_obj = file_obj[f'{instFolder}_flags'][flag]['data_table']
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# Replace values indicated by flag NaN if flag label refers to invalidated data.
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if not flag_obj['flag_code'][0].decode() is 'None':
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flag_label = ''
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else:
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flag_label = flag_obj['flag_label'][0].decode()
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if flag_label == 'I':
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t1 = pd.to_datetime(flag_obj['startdate'][0].decode(), format=flag_datetime_format)
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t2 = pd.to_datetime(flag_obj['enddate'][0].decode(), format=flag_datetime_format)
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t1_idx = abs(dataset_df[datetime_var]-t1).argmin()
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t2_idx = abs(dataset_df[datetime_var]-t2).argmin()
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dataset_df.loc[t1_idx:t2_idx,remaining_vars] = np.nan
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# Apply the .strftime() method, handling NaT values by filling with an empty string or placeholder
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dataset_df[datetime_var] = dataset_df[datetime_var].apply(
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lambda x: x.strftime(ref_datetime_format).encode('utf-8') if not pd.isnull(x) else b'' # Handle NaT/NaN by returning empty string
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)
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# Split full datasetname instFolder/fileName/datatable --> [instFolder, filename, datatable]
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dataset_name_parts = dataset_name.split('/')
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# Create new instFolder name to store dataset after applying flags
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newInstFolder = '_'.join([dataset_name_parts[0],'cleaned'])
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dataset_name_parts.remove(dataset_name_parts[0])
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# Put together relative datasetname. Note that instFolder is now missing.
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flagged_dataset_name = '/'.join(dataset_name_parts)
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dataset_dict = {'attributes':{},
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'name':flagged_dataset_name,
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'data': utils.convert_dataframe_to_np_structured_array(dataset_df)}
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dataset_dict['attributes'].update({'creation_date':utils.created_at().encode('utf-8')})
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dataset_dict['attributes'].update(dataset_obj.attrs)
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DataOpsManager.append_dataset(dataset_dict, newInstFolder)
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except Exception as e:
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self._data_ops_obj.unload_file_obj()
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print(f"An unexpected error occurred: {e}"
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"The file object has been properly closed.")
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#flag_indicator[t1_idx:t2_idx] = True
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#(datetime_var_data-t1).min()
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#if not instrument_name in file_obj and not flag_name in file_obj:
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# raise ValueError(f'Invalid instrument_name {instrument_name} and flag_name {flag_name}. No object with such names in file {self.file_path}')
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#if not f'{instrument_name}_flags':
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# raise ValueError(f'There is no flags to work with. Make sure {instrument_name}_flags is created first before running this method.')
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import dima.src.hdf5_ops as h5de
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from plotly.subplots import make_subplots
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import plotly.graph_objs as go
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import base64
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import os
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import numpy as np
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import pandas as pd
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import dima.utils.g5505_utils as utils
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UPLOAD_DIRECTORY = 'data/'
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flags_dict = {
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"000" : {"flag_label": 'V', "flag_description": "Valid measurement"},
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"100" : {"flag_label": 'V', "flag_description": "Checked by data originator. Valid measurement, overrides any invalid flags"},
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"110" : {"flag_label": 'V', "flag_description": "Episode data checked and accepted by data originator. Valid measurement"},
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"111" : {"flag_label": 'V', "flag_description": "Irregular data checked and accepted by data originator. Valid measurement"},
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"456" : {"flag_label": 'I', "flag_description": "Invalidated by data originator"},
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"460" : {"flag_label": 'I', "flag_description": "Contamination suspected"},
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"559" : {"flag_label": 'V', "flag_description": "Unspecified contamination or local influence, but considered valid"},
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"599" : {"flag_label": 'I', "flag_description": "Unspecified contamination or local influence"},
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"652" : {"flag_label": 'V', "flag_description": "construction/activity nearby"},
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"659" : {"flag_label": 'I', "flag_description": "Unspecified instrument/sampling anomaly"},
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"660" : {"flag_label": 'V', "flag_description": "Unspecified instrument/sampling anomaly"},
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"999" : {"flag_label": 'I', "flag_description": "Missing measurement, unspecified reason"}
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}
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def save_file(name, content):
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# Decode the content and save the file
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content_type, content_string = content.split(',')
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decoded = base64.b64decode(content_string)
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file_path = os.path.join(UPLOAD_DIRECTORY, name)
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if not os.path.exists(file_path):
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with open(file_path, "wb") as f:
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f.write(decoded)
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print(f"File saved successfully at {file_path}")
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return file_path
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else:
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print(f'File already exists at {file_path}.\nTo maintain the integrity of the existing file, it will not be overwritten.')
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return file_path
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def filter_flags_by_label(flags_dict, label):
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"""
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Filters the flags dictionary by the specified label.
|
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|
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Parameters:
|
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-----------
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flags_dict (dict): The dictionary containing flags.
|
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label (str): The label to filter by ('I' or 'V').
|
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|
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Returns:
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--------
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list: A list of dictionaries with 'label' and 'value' for the specified label.
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"""
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return [{'label': value['flag_description'], 'value': code}
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for code, value in flags_dict.items() if value['flag_label'] == label]
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def create_loaded_file_figure(file_path, instFolder, dataset_name, datetime_var, datetime_var_format, variables):
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DataOpsAPI = h5de.HDF5DataOpsManager(file_path)
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if not DataOpsAPI.file_obj:
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DataOpsAPI.load_file_obj()
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#target_channels = DataOpsAPI.file_obj[instfolder].attrs['target_channels']['names'][0].decode().split(',')
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#target_loc = DataOpsAPI.file_obj[instfolder].attrs['target_channels']['location'][0].decode()
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#diagnostic_channels = DataOpsAPI.file_obj[instfolder].attrs['diagnostic_channels']['names'][0].decode().split(',')
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#diagnostic_loc = DataOpsAPI.file_obj[instfolder].attrs['diagnostic_channels']['location'][0].decode()
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#fig = make_subplots(rows=(len(target_channels+diagnostic_channels)-2), cols=1, shared_xaxes=True,
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# row_heights = [1 for i in range(len(target_channels+diagnostic_channels)-2)])
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fig = make_subplots(rows=(len(variables)), cols=1,
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row_heights = [1 for i in range(len(variables))])
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traces = []
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trace_idx = 1
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dataset = DataOpsAPI.file_obj[dataset_name]
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time_column = DataOpsAPI.reformat_datetime_column(dataset_name,
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datetime_var,
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datetime_var_format)
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#time_column = dataset[datetime_var][:]
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for i in range(1,len(variables)):
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fig.add_trace(go.Scatter(x = time_column,
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y = dataset[variables[i]][:],
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mode = 'lines',
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name = variables[i]), row=trace_idx, col=1)
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fig.update_yaxes(title_text= variables[i], row=trace_idx, col=1)
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trace_idx = trace_idx + 1
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#dataset = DataOpsAPI.file_obj[diagnostic_loc]
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#time_column = DataOpsAPI.reformat_datetime_column(diagnostic_loc,diagnostic_channels[0],'%d.%m.%Y %H:%M:%S')
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#for i in range(1,len(diagnostic_channels)):
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|
||||
# fig.add_trace(go.Scatter(x = time_column,
|
||||
# y = dataset[diagnostic_channels[i]][:],
|
||||
# mode = 'lines',
|
||||
# name = diagnostic_channels[i]), row=trace_idx, col=1)
|
||||
# fig.update_yaxes(title_text= diagnostic_channels[i], row=trace_idx, col=1, type="log")
|
||||
# trace_idx = trace_idx + 1
|
||||
|
||||
fig.update_layout(height=1200, title_text=f"{instFolder} : Target and Diagnostic Channels", showlegend=False)
|
||||
|
||||
DataOpsAPI.unload_file_obj()
|
||||
#target_channels.remove(target_channels[0])
|
||||
#diagnostic_channels.remove(diagnostic_channels[0])
|
||||
return fig , [','.join([item,dataset_name]) for item in variables] #+ [','.join([item,diagnostic_loc]) for item in diagnostic_channels]
|
||||
|
||||
#import os
|
||||
import json
|
||||
import h5py
|
||||
|
||||
def load_flags(flagFolderPath, dry_run : bool = False): #filePath, instFolder, dry_run : bool = False):
|
||||
"""
|
||||
Returns a list of flags (dictionaries) based on the provided filePath and instFolder.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
filePath (str): The path to the uploaded file, expected to have an .h5 extension.
|
||||
instFolder (str): The name of the instrument folder, which must exist as a group in the HDF5 file.
|
||||
dry_run (bool): If True, performs all operations except loading file contents.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
list: A list of dictionaries containing flag data (or file paths in dry_run mode),
|
||||
or None if conditions are not met.
|
||||
"""
|
||||
|
||||
# Return None if the flags folder does not exist
|
||||
if not os.path.exists(flagFolderPath):
|
||||
return None
|
||||
|
||||
# List files in the flags folder
|
||||
files = [os.path.join(flagFolderPath, f) for f in os.listdir(flagFolderPath)]
|
||||
|
||||
# If no files found, return None
|
||||
if not files:
|
||||
return None
|
||||
|
||||
# Sort files by creation time
|
||||
sortedFiles = sorted(files, key=os.path.getctime)
|
||||
|
||||
if dry_run:
|
||||
print(f"Dry run: Found {len(sortedFiles)} files in the flags folder:")
|
||||
for filePath in sortedFiles:
|
||||
print(f" - {filePath}")
|
||||
return sortedFiles # Return file paths in dry run mode
|
||||
|
||||
# Process and load JSON files
|
||||
flagDataList = []
|
||||
for filePath in sortedFiles:
|
||||
if filePath.endswith('.json'):
|
||||
try:
|
||||
with open(filePath, 'r') as file:
|
||||
flagDataList.append(json.load(file))
|
||||
except (json.JSONDecodeError, FileNotFoundError) as e:
|
||||
print(f"Error loading file {filePath}: {e}")
|
||||
continue # Skip invalid or missing files
|
||||
|
||||
return flagDataList
|
||||
|
||||
class FlaggingAppDataManager():
|
||||
|
||||
def __init__(self, file_path, mode = 'r+') -> None:
|
||||
|
||||
self.file_path = file_path
|
||||
self.mode = mode
|
||||
self._data_ops_obj = None
|
||||
self.file_obj = None
|
||||
self.datasets_metadata_df = None
|
||||
|
||||
return None
|
||||
|
||||
def load_file_obj(self):
|
||||
self._data_ops_obj = h5de.HDF5DataOpsManager(self.file_path, self.mode)
|
||||
self._data_ops_obj.load_file_obj()
|
||||
self.file_obj = self._data_ops_obj.file_obj
|
||||
|
||||
def unload_file_obj(self):
|
||||
self._data_ops_obj = h5de.HDF5DataOpsManager(self.file_path, self.mode)
|
||||
self._data_ops_obj.unload_file_obj() # sets __data_ops_obj.file_obj to None
|
||||
|
||||
def transfer_flags(self):
|
||||
|
||||
if self.file_obj is None:
|
||||
raise RuntimeError("File object is not loaded. Please load the HDF5 file using the 'load_file_obj' method before attempting to modify it.")
|
||||
|
||||
path_to_append_dir, ext = os.path.splitext(self.file_path)
|
||||
self._data_ops_obj.update_file(path_to_append_dir)
|
||||
|
||||
|
||||
def apply_flags(self,instFolder):
|
||||
|
||||
# TODO: apply flags to diagnostic and indivial channels. so far is all channels are cleaned
|
||||
|
||||
if self.file_obj is None:
|
||||
raise RuntimeError("File object is not loaded. Please load the HDF5 file using the 'load_file_obj' method before attempting to modify it.")
|
||||
|
||||
DataOpsManager = self._data_ops_obj
|
||||
file_obj = self.file_obj
|
||||
|
||||
#with h5py.File(self.file_path, mode = self.mode, track_order=True) as file_obj:
|
||||
try:
|
||||
|
||||
if not instFolder in file_obj:
|
||||
raise ValueError(f'Invalid instrument name. Instrument folder {instFolder} was not found in file {self.file_path}.')
|
||||
|
||||
if '_'.join([instFolder,'flags']) not in flag_obj:
|
||||
raise RuntimeWarning(f'There is no flags to apply. ')
|
||||
|
||||
if not ('diagnostic_channels' in file_obj[instFolder].attrs and 'target_channels' in file_obj[instFolder].attrs):
|
||||
raise ValueError(
|
||||
f'Required attributes missing. Instrument folder {instFolder} in file {self.file_path} has to be annotated with '
|
||||
'attributes "diagnostic_channels" and "target_channels" that specify channels location and their names.'
|
||||
)
|
||||
|
||||
dataset_name = file_obj[instFolder].attrs['target_channels']['location'][0].decode()
|
||||
channel_names = file_obj[instFolder].attrs['target_channels']['names'][0].decode().split(',')
|
||||
|
||||
dataset_obj = file_obj[dataset_name]
|
||||
# TODO: maybe we can do this directly on dataset = dataset_obj[...], which is a structured numpy array, instead of wrapping that as dataframe
|
||||
dataset_df = DataOpsManager.extract_dataset_as_dataframe(dataset_name)
|
||||
|
||||
# Define datetime variable based on channel names. We assume by design the first entry of the list is the datetime variable name.
|
||||
datetime_var = channel_names[0]
|
||||
remaining_vars = channel_names.copy()
|
||||
remaining_vars.remove(datetime_var)
|
||||
|
||||
ref_datetime_format = dataset_obj.attrs.get(datetime_var,None)['datetime_format'][0].decode()
|
||||
|
||||
#datetime_var_data = pd.Series([item.decode() for item in dataset_obj[datetime_var]])
|
||||
#datetime_var_data = pd.to_datetime(datetime_var_data , format = ref_datetime_format, errors = 'coerce')
|
||||
dataset_df[datetime_var] = dataset_df[datetime_var].apply(lambda x: x.decode() )
|
||||
dataset_df[datetime_var] = pd.to_datetime(dataset_df[datetime_var], format = ref_datetime_format, errors = 'coerce')
|
||||
|
||||
flag_indicator = np.zeros(shape = dataset_df[datetime_var].shape,
|
||||
dtype = bool)
|
||||
|
||||
# TODO: include this information as part of the flag's attributes in the flag recording process
|
||||
flag_datetime_format='%Y-%m-%d %H:%M:%S.%f'
|
||||
for flag in file_obj[f'{instFolder}_flags']:
|
||||
flag_obj = file_obj[f'{instFolder}_flags'][flag]['data_table']
|
||||
|
||||
# Replace values indicated by flag NaN if flag label refers to invalidated data.
|
||||
if not flag_obj['flag_code'][0].decode() is 'None':
|
||||
flag_label = ''
|
||||
else:
|
||||
flag_label = flag_obj['flag_label'][0].decode()
|
||||
|
||||
if flag_label == 'I':
|
||||
t1 = pd.to_datetime(flag_obj['startdate'][0].decode(), format=flag_datetime_format)
|
||||
t2 = pd.to_datetime(flag_obj['enddate'][0].decode(), format=flag_datetime_format)
|
||||
|
||||
t1_idx = abs(dataset_df[datetime_var]-t1).argmin()
|
||||
t2_idx = abs(dataset_df[datetime_var]-t2).argmin()
|
||||
|
||||
dataset_df.loc[t1_idx:t2_idx,remaining_vars] = np.nan
|
||||
|
||||
|
||||
# Apply the .strftime() method, handling NaT values by filling with an empty string or placeholder
|
||||
dataset_df[datetime_var] = dataset_df[datetime_var].apply(
|
||||
lambda x: x.strftime(ref_datetime_format).encode('utf-8') if not pd.isnull(x) else b'' # Handle NaT/NaN by returning empty string
|
||||
)
|
||||
|
||||
|
||||
# Split full datasetname instFolder/fileName/datatable --> [instFolder, filename, datatable]
|
||||
dataset_name_parts = dataset_name.split('/')
|
||||
# Create new instFolder name to store dataset after applying flags
|
||||
newInstFolder = '_'.join([dataset_name_parts[0],'cleaned'])
|
||||
dataset_name_parts.remove(dataset_name_parts[0])
|
||||
# Put together relative datasetname. Note that instFolder is now missing.
|
||||
flagged_dataset_name = '/'.join(dataset_name_parts)
|
||||
|
||||
dataset_dict = {'attributes':{},
|
||||
'name':flagged_dataset_name,
|
||||
'data': utils.convert_dataframe_to_np_structured_array(dataset_df)}
|
||||
|
||||
dataset_dict['attributes'].update({'creation_date':utils.created_at().encode('utf-8')})
|
||||
dataset_dict['attributes'].update(dataset_obj.attrs)
|
||||
|
||||
|
||||
DataOpsManager.append_dataset(dataset_dict, newInstFolder)
|
||||
|
||||
except Exception as e:
|
||||
self._data_ops_obj.unload_file_obj()
|
||||
print(f"An unexpected error occurred: {e}"
|
||||
"The file object has been properly closed.")
|
||||
|
||||
|
||||
|
||||
|
||||
#flag_indicator[t1_idx:t2_idx] = True
|
||||
#(datetime_var_data-t1).min()
|
||||
|
||||
#if not instrument_name in file_obj and not flag_name in file_obj:
|
||||
# raise ValueError(f'Invalid instrument_name {instrument_name} and flag_name {flag_name}. No object with such names in file {self.file_path}')
|
||||
#if not f'{instrument_name}_flags':
|
||||
# raise ValueError(f'There is no flags to work with. Make sure {instrument_name}_flags is created first before running this method.')
|
||||
|
||||
|
||||
Reference in New Issue
Block a user