pythainlp.tools

The pythainlp.tools module encompasses a collection of miscellaneous functions primarily designed for internal use within the PyThaiNLP library. While these functions may not be directly exposed for external use, understanding their purpose can offer insights into the inner workings of PyThaiNLP.

Modules

pythainlp.tools.get_full_data_path(path: str) → str[source]

This function joins path of pythainlp data directory and the given path, and returns the full path.

Returns:: full path given the name of dataset
Return type:: str
Example:

from pythainlp.tools import get_full_data_path

get_full_data_path('ttc_freq.txt')
# output: '/root/pythainlp-data/ttc_freq.txt'

Retrieves the full path to the PyThaiNLP data directory. This function is essential for internal data management, enabling PyThaiNLP to locate resources efficiently.

pythainlp.tools.get_pythainlp_data_path() → str[source]

Returns the full path where PyThaiNLP keeps its (downloaded) data. If the directory does not yet exist, it will be created. The path can be specified through the environment variable PYTHAINLP_DATA_DIR. By default, ~/pythainlp-data will be used.

Returns:: full path of directory for pythainlp downloaded data
Return type:: str
Example:

from pythainlp.tools import get_pythainlp_data_path

get_pythainlp_data_path()
# output: '/root/pythainlp-data'

Obtains the path to the PyThaiNLP data directory. This function is useful for accessing the library’s data resources for internal processes.

pythainlp.tools.get_pythainlp_path() → str[source]

This function returns full path of PyThaiNLP codes

Returns:: full path of pythainlp codes
Return type:: str
Example:

from pythainlp.tools import get_pythainlp_path

get_pythainlp_path()
# output: '/usr/local/lib/python3.6/dist-packages/pythainlp'

Returns the path to the PyThaiNLP library directory. This function is vital for PyThaiNLP’s internal operations and library management.

The pythainlp.tools module contains these functions, which are mainly intended for PyThaiNLP’s internal workings. While they may not be directly utilized by external users, they play a pivotal role in ensuring the smooth operation of the library. Understanding the purpose of these functions can be valuable for contributors and developers working on PyThaiNLP, as it sheds light on the internal mechanisms and data management within the library.