Custom Importer

class CustomImporter

A flexible importer for custom data formats in thermal analysis.

This class provides methods to import data from non-standard file formats, offering customizable options for data parsing and preprocessing.

__init__(file_path: str, column_names: List[str], separator: str = ',', decimal: str = '.', encoding: str = 'utf-8', skiprows: int = 0)

Initialize the CustomImporter.

Parameters:

• file_path – Path to the data file.

• column_names – List of column names in the order they appear in the file.

• separator – Column separator in the file. Defaults to ‘,’.

• decimal – Decimal separator used in the file. Defaults to ‘.’.

• encoding – File encoding. Defaults to ‘utf-8’.

• skiprows – Number of rows to skip at the beginning of the file. Defaults to 0.

import_data() → Dict[str, np.ndarray]

Import data from the file.

Returns:

Dictionary containing the imported data.

Return type:

Dict[str, np.ndarray]

Raises:

• ValueError – If the file format is not recognized or supported.

• FileNotFoundError – If the specified file does not exist.

static detect_delimiter(file_path: str, num_lines: int = 5) → str

Attempt to detect the delimiter used in the file.

Parameters:

• file_path – Path to the data file.

• num_lines – Number of lines to check. Defaults to 5.

Returns:

Detected delimiter.

Return type:

str

Raises:

ValueError – If unable to detect the delimiter.

static suggest_column_names(file_path: str, delimiter: str | None = None) → List[str]

Suggest column names based on the first row of the file.

Parameters:

• file_path – Path to the data file.

• delimiter – Delimiter to use. If None, will attempt to detect.

Returns:

Suggested column names.

Return type:

List[str]

Raises:

ValueError – If unable to suggest column names.

Usage Example

from pkynetics.data_import import CustomImporter

# Detect delimiter and suggest column names
delimiter = CustomImporter.detect_delimiter('path/to/custom_data.csv')
suggested_columns = CustomImporter.suggest_column_names('path/to/custom_data.csv', delimiter=delimiter)

print(f"Detected delimiter: {delimiter}")
print(f"Suggested columns: {suggested_columns}")

# Initialize the CustomImporter
importer = CustomImporter(
    'path/to/custom_data.csv',
    suggested_columns,
    separator=delimiter,
    decimal='.',
    encoding='utf-8',
    skiprows=1
)

# Import the data
data = importer.import_data()

# Access the imported data
for column in suggested_columns:
    print(f"{column}: {data[column][:5]}...")  # Print first 5 values of each column

Key Features

1. Flexible data import for non-standard formats

2. Automatic delimiter detection

3. Column name suggestion

4. Customizable import parameters (separator, decimal format, encoding, etc.)

5. Robust error handling

Notes

• The CustomImporter is particularly useful when dealing with data formats not covered by the standard TGA and DSC importers.

• It’s recommended to use the detect_delimiter and suggest_column_names methods before initializing the CustomImporter to ensure correct data parsing.

• Make sure to specify the correct decimal separator and encoding to avoid data misinterpretation.

See Also

• tga_importer(): For importing standard Thermogravimetric Analysis (TGA) data

• dsc_importer(): For importing standard Differential Scanning Calorimetry (DSC) data