access_files Module Documentation

These functions access files, set up file structures for working with sEMG signals, and load EMGFlow's built-in dataset. The functions are mostly used internally by the package.

Module Structure

make_paths

Description

Generates a file structure for signal files, and returns a dictionary of the locations for these files.

Creates 'Raw', 'Notch', 'Bandpass', 'Smooth' and 'Feature' subfolders at a given location. If no path is given, will create a 'Data' folder in the current working directory, with these subfolders inside.

make_paths(root=None)

Parameters

root: str, optional (None)

• Root of the data to be generated. The default is None.

Returns

path_names: dict-str

• A dictionary of file locations with keys for the stage in the processing pipeline.

Example

# Create folders and get locations
path_names = EMGFlow.make_paths()

make_sample_data

Description

Generates sample data in the 'Raw' folder of a provided dictionary of file locations.

Creates '01' and '02' folders, which each contain two sample data files ('01/sample_data_01.csv', '01/sample_data_02.csv', '02/sample_data_03.csv', '02/sample_data_04.csv')

make_sample_data(path_names)

Parameters

path_names: dict-str

• Dictionary of file locations.

Raises

An exception is raised if the provided path_names dictionary doesn't contain a 'Raw' path key.

An exception is raised if the sample data cannot be loaded.

Returns

None.

Example

# Create file paths, then create sample data
path_names = EMGFlow.make_paths()
EMGFlow.make_sample_data(path_names)

read_file_type

Description

Safe wrapper for reading files of a given extension.

Switches between different reading methods based on the instructions provided.

Supported formats that can be read are: 'csv'.

read_file_type(path, file_ext)

Parameters

path: str

• Path of the file to read.

file_ext: str

• File extension to read.

Raises

An exception is raised if the file could not be read.

An exception is raised if an unsupported file format was provided for file_ext.

Returns

file: pd.DataFrame

• Returns a Pandas dataframe of the file contents.

Example

# Read a csv file
path = '/Data/Raw/01/sample_data_01.csv'
ext = 'csv'
df = EMGFlow.read_file_type(path, ext)

map_files

Description

Generate a dictionary of file names and locations (keys/values) from the subfiles of a folder.

map_files(in_path, file_ext='csv', expression=None, base=None)

Parameters

in_path: str

• The filepath to a directory to read signal files.

file_ext: str, optional ('csv')

• File extension for files to read. The default is 'csv'.

expression: str, optional (None)

• A regular expression. If provided, will only count files whose names match the regular expression. The default is None.

base: str, optional (None)

• Path of the root folder the path keys should start from. The default is None.

Raises

An exception is raised if expression is not None or a valid regular expression.

Returns

file_dirs: dict-str

• Returns a dictionary of file name and location keys/values.

Example

# Map all csv files in 'dataFiles' folder and subfolders
file_loc_1 = EMGFlow.map_files('data')

# Map all csv files in 'dataFiles' folder and subfolders
# that start with 'DATA_'
file_loc_2 = EMGFlow.map_files('data', expression='^DATA_')

convert_map_files

Description

A more advanced version of map_files that can coerce other data types into the map_files format.

If provided a dictionary (assumed to be a file location map), it is returned, filtered by expression if provided.

convert_map_files(file_obj, file_ext='csv', experssion=None)

Parameters

file_obj: str, dict-str

• The file location object. This can be a string to a file location, or an already formed dictionary of file locations.

file_ext: str, optional ('csv')

• File extension for files to read. Only reads files with this extension. The default is 'csv'.

expression: str, optional (None)

• A regular expression. If provided, will only count files whose names match the regular expression. The default is None.

Raises

An exception is raised if file_ext is an unsupported file format.

An exception is raised if expression is not None or a valid regular expression.

Returns

file_dirs: dict-str

• Returns a dictionary of file name and location keys/values.

Example

# Read in file locations normally
file_loc_1 = EMGFlow.convert_map_files('/data')

# Filter an existing dataframe with a regular expression
file_loc_2 = EMGFlow.convert_map_files(file_loc_1, expression='^01')

map_files_fuse

Description

Generate a dictionary of file names and locations from different forms of input. Each directory should contain the same file at different stages with the same name, and will create a dataframe of the location of this file in each of the directories provided.

map_files_fuse(file_dirs, names)

Parameters

file_dirs: list-dict-str

• List of file location directories.

names: list-str

• List of names to use for file directory columns. Same order as file_dirs.

Raises

An exception is raised if a file contained in the first file directory is not found in the other file directories.

Returns

path_df: pd.DataFrame

• A dataframe of file names, and their locations in each file directory.

Example

# Create file directory dictionaries
raw_path = EMGFlow.map_files('/Data/Raw')
notch_path = EMGFlow.map_files('/Data/Notch')
band_path = EMGFlow.map_files('/Data/Bandpass')

# Create dictionary list and names
file_dirs = [raw_path, notch_path, band_path]
names = ['Raw', 'Notch', 'Bandpass']

# Create data frame
dfDirs = EMGFlow.map_files_fuse(file_dirs, names)