Generic CSV Format¶

EMGIO provides a flexible CSV importer that can handle a wide variety of CSV-formatted data files, including those with or without headers, different delimiters, and various time column formats.

Features¶

Automatic detection of file format, headers, and delimiters
Support for files with or without time columns
Customizable channel properties
Intelligent format detection to recommend specialized importers when appropriate

Usage¶

from emgio import EMG

# Basic usage (automatic format detection)
emg = EMG.from_file('data.csv')

# Force using the generic CSV importer even if specialized format is detected
emg = EMG.from_file('data.csv', importer='csv', force_csv=True)

# Specify custom parameters
emg = EMG.from_file('data.csv', importer='csv', 
                   sample_frequency=1000,  # Required if no time column
                   has_header=True,        # Whether file has header row
                   delimiter=',',          # Column delimiter
                   skiprows=0,             # Rows to skip at the beginning
                   time_column='Time')     # Column to use as time index

Specialized Parameters¶

The CSV importer accepts many customization parameters:

Parameter	Description
`sample_frequency`	Sampling frequency in Hz (required if no time column)
`has_header`	Whether file has a header row (auto-detected if not specified)
`skiprows`	Number of rows to skip at beginning (auto-detected if not specified)
`delimiter`	Column delimiter (auto-detected if not specified)
`time_column`	Name or index of column to use as time index (auto-detected if not specified)
`columns`	List of column names or indices to include
`channel_names`	Custom names for channels
`channel_types`	Dict mapping column names to channel types ('EMG', 'ACC', etc.)
`physical_dimensions`	Dict mapping column names to physical dimensions
`metadata`	Dict of additional metadata to include

Examples¶

Basic CSV with Headers¶

# data.csv:
# Time,EMG1,EMG2,ACC1
# 0.000,0.1,0.2,0.3
# 0.001,0.2,0.3,0.4
# ...

emg = EMG.from_file('data.csv', importer='csv')

Headerless CSV with Custom Names¶

# data.csv:
# 0.1,0.2,0.3
# 0.2,0.3,0.4
# ...

emg = EMG.from_file('data.csv', importer='csv',
                    has_header=False,
                    sample_frequency=1000,  # Required since no time column
                    channel_names=['EMG_L', 'EMG_R', 'ACC_X'])

Setting Channel Types and Units¶

emg = EMG.from_file('data.csv', importer='csv',
                    channel_types={
                        'EMG1': 'EMG',
                        'EMG2': 'EMG',
                        'ACC1': 'ACC'
                    },
                    physical_dimensions={
                        'EMG1': 'mV',
                        'EMG2': 'mV',
                        'ACC1': 'g'
                    })

Selecting Specific Columns¶

# Only load columns 0 and 2
emg = EMG.from_file('data.csv', importer='csv',
                    columns=[0, 2],
                    sample_frequency=1000)

# Only load columns named 'EMG1' and 'ACC1'
emg = EMG.from_file('data.csv', importer='csv',
                    columns=['EMG1', 'ACC1'])

Auto-Detection Features¶

The CSV importer includes several auto-detection capabilities:

Format Detection: Recognizes specialized formats like Trigno CSV files
Delimiter Detection: Identifies the most common delimiter (comma, tab, semicolon)
Header Detection: Determines if the first row is a header based on content
Time Column Detection: Looks for columns that might represent time

Notes¶

When a specialized format (like Trigno) is detected, the importer will suggest using the appropriate specialized importer
For headerless files, auto-generated channel names will be "Channel_0", "Channel_1", etc.
If no time column is found, a sample frequency must be provided to generate a time index