Python Humdrum **kern and **mens utilities

Python package that provides comprehensive tools for working with symbolic modern and mensural notations in Humdrum format. kernpy is a fully open-source project open to contributions.

Documentation

Visit the online website: https://kernpy.pages.dev/

Index:

• Code examples
• Installation
• Documentation
• Run tests
• Contributing
• Citation

Code examples

Basic Usage

Load a **kern/**mens file into a kp.Document.

import kernpy as kp

# Read a **kern file
document, errors = kp.load("path/to/file.krn")

Load a **kern/**mens from a string into a kp.Document.

import kernpy as kp

document, errors = kp.loads("**kern\n*clefC3\n*k[b-e-a-]\n*M3/4\n4e-\n4g\n4c\n=1\n4r\n2cc;\n==\n*-")

Create a new standardized file from a kp.Document.

import kernpy as kp

kp.dump(document, "newfile.krn")

Save the document in a string from a kp.Document.

import kernpy as kp

content = kp.dumps(document)

Exploring different options when creating new files

Only use the specified spines in spine_types.

import kernpy as kp

# only export the **kern spines
kp.dump(document, "newfile_core.krn",
        spine_types=['**kern'])

# only export the **text spines
kp.dump(document, "newfile_lyrics.krn",
        spine_types=['**text])
                     
# only export **kern and **text spines     
kp.dump(document, "newfile_core_and_lyrics.krn",
        spine_types=['*+text'])

• The categories are hierarchically defined in the TokenCategory class. See the hierarchy as a tree

import kernpy as kp


print(kp.TokenCategory.tree())

Tree:

.
├── STRUCTURAL
│   ├── HEADER
│   └── SPINE_OPERATION
├── CORE
│   ├── NOTE_REST
│   │   ├── DURATION
│   │   ├── NOTE
│   │   │   ├── PITCH
│   │   │   ├── DECORATION
│   │   │   └── ALTERATION
│   │   └── REST
│   ├── CHORD
│   ├── EMPTY
│   └── ERROR
├── SIGNATURES
│   ├── CLEF
│   ├── TIME_SIGNATURE
│   ├── METER_SYMBOL
│   ├── KEY_SIGNATURE
│   └── KEY_TOKEN
├── ENGRAVED_SYMBOLS
├── OTHER_CONTEXTUAL
├── BARLINES
├── COMMENTS
│   ├── FIELD_COMMENTS
│   └── LINE_COMMENTS
├── DYNAMICS
├── HARMONY
├── FINGERING
├── LYRICS
├── INSTRUMENTS
├── IMAGE_ANNOTATIONS
│   ├── BOUNDING_BOXES
│   └── LINE_BREAK
├── OTHER
├── MHXM
└── ROOT

• Use include for selecting the **kern semantic categories to use. The output only contains what is passed. By default, all the categories are included.

import kernpy as kp


kp.dump(document, "newfile_only_clefs.krn",
        include={kp.TokenCategory.CLEF})
kp.dump(document, "newfile_only_durations_and_bounding_boxes.krn",
        include={kp.TokenCategory.DURATION, kp.TokenCategory.BOUNDING_BOXES})

• Use exclude for selecting the **kern semantic categories to not use. The output contains everything except what is passed. By default, any category is excluded.

import kernpy as kp

kp.dump(document, "newfile_without_pitches.krn",
        exclude={kp.TokenCategory.PITCH})
kp.dump(document, "newfile_without_durations_or_rests.krn",
        exclude={kp.TokenCategory.BARLINES, kp.TokenCategory.REST})

• Use include and exclude together to select the **kern semantic categories to use. The output combines both.

import kernpy as kp

kp.dump(document, "newfile_custom.krn",
        include=kp.BEKERN_CATEGORIES,  # Preloaded set of simple categories
        exclude={kp.TokenCategory.PITCH})

# Inspect the BEKERN preloaded categories
print(kp.BEKERN_CATEGORIES)

• Use encoding to select how the categories are split. By default, the normalizedKern encoding is used.

import kernpy as kp

kp.dump(document, "newfile_normalized.krn",
        encoding=kp.Encoding.normalizedKern)  # Default encoding

Select the proper Humdrum **kern encoding:

kernpy provides different encodings to export the content each symbol in different formats.

Encoding	Output	Description
kern	2.bb-_L	Traditional Humdrum **kern encoding
ekern	2@.@bb@-·_·L	Extended Humdrum **kern encoding
bkern	2.bb-	Basic Humdrum **kern encoding
bekern	2@.@bb@-	Basic Extended Humdrum **kern encoding

Use the Encoding enum class to select the encoding:

import kernpy as kp

doc, _ = kp.load('resource_dir/legacy/chor048.krn')

kern_content = kp.dumps(doc, encoding=kp.Encoding.normalizedKern)
ekern_content = kp.dumps(doc, encoding=kp.Encoding.eKern)
bkern_content = kp.dumps(doc, encoding=kp.KernTypeExporter.bKern)
bekern_content = kp.dumps(doc, encoding=kp.KernTypeExporter.bEkern)

• Use from_measure and to_measure to select the measures to export. By default, all the measures are exported.

import kernpy as kp

kp.dump(document, "newfile_1_to_10.krn",
        from_measure=1,  # First from measure 1
        to_measure=10)   # Last measure exported

• Use spine_ids to select the spines to export. By default, all the spines are exported.

import kernpy as kp

kp.dump(document, "newfile_1_and_2.krn",
        spine_ids=[0, 1])  # Export only the first and the second spine

• Use show_measure_numbers to select if the measure numbers are shown. By default, the measure numbers are shown.

import kernpy as kp

kp.dump(document, "newfile_no_measure_numbers.krn",
        show_measure_numbers=False)  # Do not show measure numbers

• Use all the options at the same time.

import kernpy as kp

kp.dump(document, "newfile.krn",
        spine_types=['**kern'],  # Export only the **kern spines
        include=kp.BEKERN_CATEGORIES,  # Token categories to include
        exclude={kp.TokenCategory.PITCH},  # Token categories to exclude
        encoding=kp.Encoding.eKern,  # Kern encoding
        from_measure=1,  # First from measure 1
        to_measure=10,  # Last measure exported
        spine_ids=[0, 1],  # Export only the first and the second spine
        show_measure_numbers=False,  # Do not show measure numbers
        )

Exploring kernpy utilities.

• Spines analysis Retrieve all the spine types of the document.

import kernpy as kp

kp.spine_types(document)
# ['**kern', '**kern', '**kern', '**kern', '**root', '**harm']

kp.spine_types(document, spine_types=None)
# ['**kern', '**kern', '**kern', '**kern', '**root', '**harm']

kp.spine_types(document, spine_types=['**kern'])
# ['**kern', '**kern', '**kern', '**kern']

• Get specific **kern spines.

import kernpy as kp

def how_many_instrumental_spines(document):
    print(kp.spine_types(document, ['**kern']))
    return len(kp.spine_types(document, ['**kern']))
# ['**kern', '**kern', '**kern', '**kern']
# 4

def has_voice(document):
    return len(kp.spine_types(document, ['**text'])) > 0
# True

How many measures are there in the document? Which measures do you want to export?

After reading the score into the Document object. You can get some useful data:

first_measure: int = document.get_first_measure()
last_measure: int = document.measures_count()

Iterate over all the measures of the document.

import kernpy as kp

doc, _ = kp.load('resource_dir/legacy/chor048.krn')  # 10 measures score
for i in range(doc.get_first_measure(), doc.measures_count(), 1):  # from 1 to 11, step 1
    # Export only the i-th measure (1 long measure scores)
    content_ith_measure = kp.dumps(doc, from_measure=i, to_measure=i)
    
    # Export the i-th measure and the next 4 measures (5 long measure scores)
    if i + 4 <= doc.measures_count():
        content_longer = kp.dumps(doc, from_measure=i, to_measure=i + 4)
    ...

It is easier to iterate over all the measures using the for measure in doc: loop (using the __ iter__ method):

import kernpy as kp

for measure in doc:
    content = kp.dumps(doc, from_measure=measure, to_measure=measure)
    ...

Exploring the page bounding boxes.

import kernpy as kp

# Iterate over the pages using the bounding boxes
doc, _ = kp.load('kern_having_bounding_boxes.krn')

# Inspect the bounding boxes
print(doc.page_bounding_boxes)


def are_there_bounding_boxes(doc):
   return len(doc.get_all_tokens(filter_by_categories=[kp.TokenCategory.BOUNDING_BOXES])) > 0


# True

# Iterate over the pages
for page_label, bounding_box_measure in doc.page_bounding_boxes.items():
   print(f"Page: {page_label}"
         f"Bounding box: {bounding_box_measure}"
         f"from_measure: {bounding_box_measure.from_measure}"
         f"to_measure+1: {bounding_box_measure.to_measure}")  # TODO: Check bounds
   kp.dump(doc, f"foo_{page_label}.ekrn",
           spine_types=['**kern'],
           token_categories=kp.BEKERN_CATEGORIES,
           encoding=kp.Encoding.eKern,
           from_measure=bounding_box_measure.from_measure,
           to_measure=bounding_box_measure.to_measure - 1  # TODO: Check bounds            
           )

Merge different full kern scores

import kernpy as kp
# NOT AVAILABLE YET!!!
# Pay attention to `kp.merge` too.

# Concat two valid documents
score_a = '**kern\n*clefG2\n=1\n4c\n4d\n4e\n4f\n*-\n'
score_b = '**kern\n*clefG2\n=1\n4a\n4c\n4d\n4c\n*-\n'
concatenated = kp.merge([score_a, score_b])

Concatenate sorted fragments of the same score

import kernpy as kp

fragment_a = '**kern\n*clefG2\n=1\n4c\n4d\n4e\n4f\n*-\n'
fragment_b = '=2\n4a\n4c\n4d\n4c\n*-\n=3\n4a\n4c\n4d\n4c\n*-\n'
fragment_c = '=4\n4a\n4c\n4d\n4c\n*-\n=5\n4a\n4c\n4d\n4c\n*-\n'
fragment_d = '=6\n4a\n4c\n4d\n4c\n*-\n=7\n4a\n4c\n4d\n4c\n*-\n==*-'
fragments = [fragment_a, fragment_b, fragment_c, fragment_d]

doc_merged, indexes = kp.concat(fragments)
for index_pair in indexes:
    from_measure, to_measure = index_pair
    print(f'From measure: {from_measure}, To measure: {to_measure}')
    print(kp.dumps(doc_merged, from_measure=from_measure, to_measure=to_measure))

# Sometimes is useful having a different separator between the fragments rather than the default one (newline)...
doc_merged, indexes = kp.concat(fragments, separator='')

Inspect the Document class functions

import kernpy as kp
doc, _ = kp.load('resource_dir/legacy/chor048.krn')  # 10 measures score

frequencies = doc.frequencies()  # All the token categories
filtered_frequencies = doc.frequencies(filter_by_categories=[kp.TokenCategory.SIGNATURES])
frequencies['*k[f#c#]']
# {
#   'occurrences': 4,
#   'category': SIGNATURES,
# }

# Get all the tokens in the document
all_tokens: [kp.Token] = doc.get_all_tokens()
all_tokens_encodings: [str] = doc.get_all_tokens_encodings()

# Get the unique tokens in the document (vocabulary)
unique_tokens: [kp.Token] = doc.get_unique_tokens()
unique_token_encodings: [str] = doc.get_unique_token_encodings()

# Get the line comments in the document
document.get_metacomments()
# ['!!!COM: Coltrane', '!!!voices: 1', '!!!OPR: Blue Train']
document.get_metacomments(KeyComment='COM')
# ['!!!COM: Coltrane']
document.get_metacomments(KeyComment='COM', clear=True)
# ['Coltrane']
document.get_metacomments(KeyComment='non_existing_key')
# []

Transpose

• Inspect what intervals are available for transposing.

import kernpy as kp

print(kp.AVAILABLE_INTERVALS)

• Transpose the document to a specific interval.

import kernpy as kp

doc, err = kp.load('resource_dir/legacy/chor048.krn')  # 10 measures score
higher_octave_doc = doc.to_transposed('octave', 'up')

kp.dump(higher_octave_doc, 'higher_octave.krn')

On your own

• Handle the document if needed.

import kernpy as kp

# Access the document tree
print(document.tree)
# <kernpy.core.document.DocumentTree object at 0x7f8b3b3b3d30>

# View the tree-based Document structure for debugging.
kp.graph(document, '/tmp/graph.dot')
# Render the graph 
# - using Graphviz extension in your IDE
# - in the browser here: https://dreampuf.github.io/GraphvizOnline/

Installation

Production version:

Just install the last version of kernpy using pip:

pip3 install kernpy

# ensure you have the latest version
pip3 install kernpy --upgrade 

Note

This module is downloaded by default in the /tmp directory in Linux. So it is removed when shutdown the machine.

---

Documentation

Documentation available at https://kernpy.pages.dev/

kernpy also supports been executed as a module. Find out the available commands:

python -m kernpy --help
python -m kernpy <command> <options>

Run tests:

cd tests && python -m pytest

Contributing

We welcome contributions from the community! If you'd like to contribute to the project, please follow these steps:

Go to the file CONTRIBUTING.md for more information on how to contribute.

Citation:

@inproceedings{kernpy_mec_2025,
  title={{kernpy: a Humdrum **Kern Oriented Python Package for Optical Music Recognition Tasks}},
  author={Cerveto-Serrano, Joan and Rizo, David and Calvo-Zaragoza, Jorge},
  booktitle={{Proceedings of the Music Encoding Conference (MEC2025)}},
  address={London, United Kingdom},
  year={2025}
}