Added .strip to column names to remove unwanted characters (\r|\t|\n) and included units description to timestamps.
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@ -165,7 +165,7 @@ def read_txt_files_as_dict(filename : str , work_with_copy : bool = True ):
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if table_header in line.decode(file_encoding):
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list_of_substrings = line.decode(file_encoding).split(separator)
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data_start = True
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column_names = [str(i)+'_'+name for i, name in enumerate(list_of_substrings)]
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column_names = [str(i)+'_'+name.strip() for i, name in enumerate(list_of_substrings)]
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#column_names = []
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#for i, name in enumerate(list_of_substrings):
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# column_names.append(str(i)+'_'+name)
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@ -270,29 +270,19 @@ def read_txt_files_as_dict(filename : str , work_with_copy : bool = True ):
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except ValueError as err:
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print(err)
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#dataset = {}
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#numerical_variables= [item.encode("utf-8") for item in numerical_variables]
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#dataset['name'] = 'numerical_variable_names'
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#dataset['data'] = np.array(numerical_variables).reshape((1,len(numerical_variables)))
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#dataset['shape'] = dataset['data'].shape
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#dataset['dtype'] = type(dataset['data'])
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#file_dict['datasets'].append(dataset)
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if categorical_variables:
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dataset = {}
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dataset['name'] = 'table_categorical_variables'
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dataset['data'] = dataframe_to_np_structured_array(df_categorical_attrs) #df_categorical_attrs.loc[:,categorical_variables].to_numpy()
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dataset['shape'] = dataset['data'].shape
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dataset['dtype'] = type(dataset['data'])
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file_dict['datasets'].append(dataset)
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if 'timestamps' in categorical_variables:
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dataset['attributes'] = {'timestamps': metadata.parse_attribute({'unit':'YYYY-MM-DD HH:MM:SS.ffffff'})}
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file_dict['datasets'].append(dataset)
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# dataset = {}
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# categorical_variables = [item.encode("utf-8") for item in categorical_variables]
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# dataset['name'] = 'categorial_variable_names'
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# dataset['data'] = np.array(categorical_variables).reshape((1,len(categorical_variables)))
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# dataset['shape'] = dataset['data'].shape
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# dataset['dtype'] = type(dataset['data'])
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# file_dict['datasets'].append(dataset)
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except:
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return {}
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