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Utilities

CustomJSONEncoder

Bases: JSONEncoder

Custom JSON encoder that: 1. Converts numpy arrays/matrices to lists 2. Cleans numerical values (removes near-zero noise, rounds to precision) 3. Converts sets to lists

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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class CustomJSONEncoder(json.JSONEncoder):
    """
    Custom JSON encoder that:
    1. Converts numpy arrays/matrices to lists
    2. Cleans numerical values (removes near-zero noise, rounds to precision)
    3. Converts sets to lists
    """

    def __init__(
        self, *args: Any, clean_numerics: bool = True, threshold: float = 1e-10, decimals: int = 8, **kwargs: Any
    ):
        """
        Args:
            clean_numerics: If True, clean numeric values (default True)
            threshold: Values below this are set to 0 (default 1e-10)
            decimals: Number of decimal places to round to (default 8)
        """
        super().__init__(*args, **kwargs)
        self.clean_numerics = clean_numerics
        self.threshold = threshold
        self.decimals = decimals

    def encode(self, obj: Any) -> str:
        """Override encode to clean numerics in the entire structure."""
        if self.clean_numerics:
            obj = self._clean_object(obj)
        return super().encode(obj)

    def _clean_object(self, obj: Any) -> Any:
        """Recursively clean numeric values in any object."""
        if isinstance(obj, dict):
            return {k: self._clean_object(v) for k, v in obj.items()}
        elif isinstance(obj, (list, tuple)):
            return [self._clean_object(item) for item in obj]
        elif isinstance(obj, (float, np.floating)):
            return clean_numeric_value(float(obj), self.threshold, self.decimals)
        elif isinstance(obj, (int, np.integer)):
            return int(obj)
        elif isinstance(obj, (np.ndarray, np.matrix)):
            # Convert to list and clean
            return clean_numeric_list(obj.tolist(), self.threshold, self.decimals)
        elif isinstance(obj, set):
            return [self._clean_object(item) for item in obj]
        else:
            return obj

    def default(self, obj: Any) -> Any:
        """Handle non-serializable objects."""
        if isinstance(obj, (np.ndarray, np.matrix)):
            cleaned = clean_numeric_list(obj.tolist(), self.threshold, self.decimals)
            return cleaned
        if isinstance(obj, set):
            return list(obj)
        return super().default(obj)

__init__(*args, clean_numerics=True, threshold=1e-10, decimals=8, **kwargs)

Parameters:

Name Type Description Default
clean_numerics bool

If True, clean numeric values (default True)

 True
threshold float

Values below this are set to 0 (default 1e-10)

 1e-10
decimals int

Number of decimal places to round to (default 8)

 8
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def __init__(
    self, *args: Any, clean_numerics: bool = True, threshold: float = 1e-10, decimals: int = 8, **kwargs: Any
):
    """
    Args:
        clean_numerics: If True, clean numeric values (default True)
        threshold: Values below this are set to 0 (default 1e-10)
        decimals: Number of decimal places to round to (default 8)
    """
    super().__init__(*args, **kwargs)
    self.clean_numerics = clean_numerics
    self.threshold = threshold
    self.decimals = decimals

default(obj)

Handle non-serializable objects.

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def default(self, obj: Any) -> Any:
    """Handle non-serializable objects."""
    if isinstance(obj, (np.ndarray, np.matrix)):
        cleaned = clean_numeric_list(obj.tolist(), self.threshold, self.decimals)
        return cleaned
    if isinstance(obj, set):
        return list(obj)
    return super().default(obj)

encode(obj)

Override encode to clean numerics in the entire structure.

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def encode(self, obj: Any) -> str:
    """Override encode to clean numerics in the entire structure."""
    if self.clean_numerics:
        obj = self._clean_object(obj)
    return super().encode(obj)

clean_json_numerics(data, threshold=1e-10, decimals=8)

Recursively clean numeric values in a JSON-like data structure.

Parameters:

Name Type Description Default
data Any

JSON data (dict, list, or scalar)

 required
threshold float

Values below this are set to 0

 1e-10
decimals int

Number of decimal places to round to

 8

Returns:

Type Description
Any

Cleaned data structure
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def clean_json_numerics(data: Any, threshold: float = 1e-10, decimals: int = 8) -> Any:
    """
    Recursively clean numeric values in a JSON-like data structure.

    Args:
        data: JSON data (dict, list, or scalar)
        threshold: Values below this are set to 0
        decimals: Number of decimal places to round to

    Returns:
        Cleaned data structure
    """
    if isinstance(data, dict):
        return {k: clean_json_numerics(v, threshold, decimals) for k, v in data.items()}
    elif isinstance(data, list):
        return [clean_json_numerics(item, threshold, decimals) for item in data]
    elif isinstance(data, float):
        return clean_numeric_value(data, threshold, decimals)
    else:
        return data

clean_name_for_urdf(name)

Clean a name to be URDF-safe by replacing problematic characters.

This is similar to get_sanitized_name but specifically for URDF compatibility, following the reference implementation's approach.

Parameters:

Name Type Description Default
name str

Name to clean for URDF compatibility.

 required

Returns:

Type Description
str

URDF-safe name.

Examples:

>>> clean_name_for_urdf("wheel <1>")
"wheel_(1)"
>>> clean_name_for_urdf("joint/arm\link")
"joint_arm_link"
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def clean_name_for_urdf(name: str) -> str:
    """
    Clean a name to be URDF-safe by replacing problematic characters.

    This is similar to get_sanitized_name but specifically for URDF compatibility,
    following the reference implementation's approach.

    Args:
        name: Name to clean for URDF compatibility.

    Returns:
        URDF-safe name.

    Examples:
        >>> clean_name_for_urdf("wheel <1>")
        "wheel_(1)"
        >>> clean_name_for_urdf("joint/arm\\link")
        "joint_arm_link"
    """
    name = name.replace("<", "(").replace(">", ")")
    name = re.sub(r"\s+", "_", name)
    name = re.sub(r"[/\\]+", "_", name)
    return name

clean_numeric_list(data, threshold=1e-10, decimals=8)

Recursively clean numeric values in nested lists/arrays.

Parameters:

Name Type Description Default
data Any

Data structure (can be list, nested list, or scalar)

 required
threshold float

Values with absolute value below this are set to 0

 1e-10
decimals int

Number of decimal places to round to

 8

Returns:

Type Description
Any

Cleaned data structure
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def clean_numeric_list(data: Any, threshold: float = 1e-10, decimals: int = 8) -> Any:
    """
    Recursively clean numeric values in nested lists/arrays.

    Args:
        data: Data structure (can be list, nested list, or scalar)
        threshold: Values with absolute value below this are set to 0
        decimals: Number of decimal places to round to

    Returns:
        Cleaned data structure
    """
    if isinstance(data, (list, tuple)):
        return [clean_numeric_list(item, threshold, decimals) for item in data]
    elif isinstance(data, (float, np.floating)):
        return clean_numeric_value(float(data), threshold, decimals)
    elif isinstance(data, (int, np.integer)):
        return int(data)
    else:
        return data

clean_numeric_value(value, threshold=1e-10, decimals=8)

Clean a numeric value by: 1. Setting values below threshold to exactly 0 2. Rounding to specified decimal places

Parameters:

Name Type Description Default
value float

The numeric value to clean

 required
threshold float

Values with absolute value below this are set to 0 (default 1e-10)

 1e-10
decimals int

Number of decimal places to round to (default 8)

 8

Returns:

Type Description
float

Cleaned numeric value

Examples:

>>> clean_numeric_value(5.62050406e-16)
0.0
>>> clean_numeric_value(0.123456789012345, decimals=5)
0.12346
>>> clean_numeric_value(-1e-11)
0.0
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def clean_numeric_value(value: float, threshold: float = 1e-10, decimals: int = 8) -> float:
    """
    Clean a numeric value by:
    1. Setting values below threshold to exactly 0
    2. Rounding to specified decimal places

    Args:
        value: The numeric value to clean
        threshold: Values with absolute value below this are set to 0 (default 1e-10)
        decimals: Number of decimal places to round to (default 8)

    Returns:
        Cleaned numeric value

    Examples:
        >>> clean_numeric_value(5.62050406e-16)
        0.0
        >>> clean_numeric_value(0.123456789012345, decimals=5)
        0.12346
        >>> clean_numeric_value(-1e-11)
        0.0
    """
    # First check if value is below threshold
    if abs(value) < threshold:
        return 0.0
    # Round to specified decimal places
    return round(value, decimals)

format_number(value)

Format a number to 8 significant figures

Parameters:

Name Type Description Default
value float

Number to format

 required

Returns:

Name Type Description
str str

Formatted number

Examples:

>>> format_number(0.123456789)
"0.12345679"

>>> format_number(123456789)
"123456789"
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def format_number(value: float) -> str:
    """
    Format a number to 8 significant figures

    Args:
        value (float): Number to format

    Returns:
        str: Formatted number

    Examples:
        >>> format_number(0.123456789)
        "0.12345679"

        >>> format_number(123456789)
        "123456789"
    """

    return f"{value:.8g}"

generate_uid(values)

Generate a 16-character unique identifier from a list of strings

Parameters:

Name Type Description Default
values list[str]

List of strings to concatenate

 required

Returns:

Name Type Description
str str

Unique identifier

Examples:

>>> generate_uid(["hello", "world"])
"c4ca4238a0b92382"
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def generate_uid(values: list[str]) -> str:
    """
    Generate a 16-character unique identifier from a list of strings

    Args:
        values (list[str]): List of strings to concatenate

    Returns:
        str: Unique identifier

    Examples:
        >>> generate_uid(["hello", "world"])
        "c4ca4238a0b92382"
    """

    _value = "".join(values)
    return hashlib.sha256(_value.encode()).hexdigest()[:16]

get_random_files(directory, file_extension, count)

Get random files from a directory with a specific file extension and count

Parameters:

Name Type Description Default
directory str

Directory path

 required
file_extension str

File extension

 required
count int

Number of files to select

 required

Returns:

Type Description
tuple[list[str], list[str]]

list[str]: List of file paths

Raises:

Type Description
ValueError

Not enough files in directory if count exceeds number of files

Examples:

>>> get_random_files("json", ".json", 1)
["json/file.json"]

>>> get_random_files("json", ".json", 2)
["json/file1.json", "json/file2.json"]
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def get_random_files(directory: str, file_extension: str, count: int) -> tuple[list[str], list[str]]:
    """
    Get random files from a directory with a specific file extension and count

    Args:
        directory (str): Directory path
        file_extension (str): File extension
        count (int): Number of files to select

    Returns:
        list[str]: List of file paths

    Raises:
        ValueError: Not enough files in directory if count exceeds number of files

    Examples:
        >>> get_random_files("json", ".json", 1)
        ["json/file.json"]

        >>> get_random_files("json", ".json", 2)
        ["json/file1.json", "json/file2.json"]
    """

    _files = [file for file in os.listdir(directory) if file.endswith(file_extension)]

    if len(_files) < count:
        raise ValueError("Not enough files in directory")

    selected_files = random.sample(_files, count)
    file_paths = [os.path.join(directory, file) for file in selected_files]

    logger.info(f"Selected files: {file_paths}")

    return file_paths, [x.split(".")[0] for x in selected_files]

get_random_names(directory, count, filename='words.txt')

Generate random names from a list of words in a file

Parameters:

Name Type Description Default
directory str

Path to directory containing words file

 required
count int

Number of random names to generate

 required
filename str

File containing list of words. Default is "words.txt"

 'words.txt'

Returns:

Type Description
list[str]

List of random names

Raises:

Type Description
ValueError

If count exceeds the number of available words

Examples:

>>> get_random_names(directory="../", count=1)
["charizard"]

>>> get_random_names(directory="../", count=2)
["charizard", "pikachu"]
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def get_random_names(directory: str, count: int, filename: str = "words.txt") -> list[str]:
    """
    Generate random names from a list of words in a file

    Args:
        directory: Path to directory containing words file
        count: Number of random names to generate
        filename: File containing list of words. Default is "words.txt"

    Returns:
        List of random names

    Raises:
        ValueError: If count exceeds the number of available words

    Examples:
        >>> get_random_names(directory="../", count=1)
        ["charizard"]

        >>> get_random_names(directory="../", count=2)
        ["charizard", "pikachu"]
    """

    words_file_path = os.path.join(directory, filename)

    with open(words_file_path) as file:
        words = file.read().splitlines()

    if count > len(words):
        raise ValueError("count exceeds the number of available words")

    return random.sample(words, count)

get_sanitized_name(name, replace_with='_', remove_onshape_tags=False)

Sanitize a name by removing special characters, preserving only the specified replacement character, and replacing spaces with it. Ensures no consecutive replacement characters in the result. Optionally preserves a trailing " " tag where n is a number.

Parameters:

Name Type Description Default
name str

Name to sanitize.

 required
replace_with str

Character to replace spaces and other special characters with (default is '_').

 '_'
remove_onshape_tags bool

If True, removes a trailing " " tag where n is a number. Default is False.

 False

Returns:

Name Type Description
str str

Sanitized name.

Examples:

>>> get_sanitized_name("wheel1 <3>")
"wheel1_3"

>>> get_sanitized_name("wheel1 <3>", remove_onshape_tags=True)
"wheel1"

>>> get_sanitized_name("wheel1 <3>", replace_with='-', remove_onshape_tags=False)
"wheel1-3"
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def get_sanitized_name(name: str, replace_with: str = "_", remove_onshape_tags: bool = False) -> str:
    """
    Sanitize a name by removing special characters, preserving only the specified
    replacement character, and replacing spaces with it. Ensures no consecutive
    replacement characters in the result.
    Optionally preserves a trailing " <n>" tag where n is a number.

    Args:
        name (str): Name to sanitize.
        replace_with (str): Character to replace spaces and other special characters with (default is '_').
        remove_onshape_tags (bool): If True, removes a trailing " <n>" tag where n is a number. Default is False.

    Returns:
        str: Sanitized name.

    Examples:
        >>> get_sanitized_name("wheel1 <3>")
        "wheel1_3"

        >>> get_sanitized_name("wheel1 <3>", remove_onshape_tags=True)
        "wheel1"

        >>> get_sanitized_name("wheel1 <3>", replace_with='-', remove_onshape_tags=False)
        "wheel1-3"
    """

    if replace_with not in "-_":
        raise ValueError("replace_with must be either '-' or '_'")

    tag = ""
    if remove_onshape_tags:
        # Regular expression to detect a trailing " <n>" where n is one or more digits
        tag_pattern = re.compile(r"\s<\d+>$")
        match = tag_pattern.search(name)
        if match:
            tag = match.group()  # e.g., " <3>"
            if tag:
                name = name[: match.start()]

    sanitized_name = "".join(char if char.isalnum() or char in "-_ " else "" for char in name)
    sanitized_name = sanitized_name.replace(" ", replace_with)
    sanitized_name = re.sub(f"{re.escape(replace_with)}{{2,}}", replace_with, sanitized_name)

    return sanitized_name

load_model_from_json(model_class, file_path, clean_numerics=True, threshold=1e-10, decimals=8)

Load a Pydantic model from a JSON file with optional numeric cleaning.

Parameters:

Name Type Description Default
model_class type[BaseModel]

The Pydantic model class to instantiate

 required
file_path str

Path to JSON file

 required
clean_numerics bool

If True, clean numeric values before validation (default True)

 True
threshold float

Values below this are set to 0 (default 1e-10)

 1e-10
decimals int

Number of decimal places to round to (default 8)

 8

Returns:

Name Type Description
BaseModel BaseModel

Instance of the model class populated from JSON

Examples:

>>> class TestModel(BaseModel):
...     a: int
...     b: str
...
>>> model = load_model_from_json(TestModel, "test.json")
>>> print(model.a, model.b)
1 hello

>>> from onshape_robotics_toolkit.models import Assembly
>>> assembly = load_model_from_json(Assembly, "assembly.json")
>>> assembly = load_model_from_json(Assembly, "assembly.json", decimals=5)
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def load_model_from_json(
    model_class: type[BaseModel],
    file_path: str,
    clean_numerics: bool = True,
    threshold: float = 1e-10,
    decimals: int = 8,
) -> BaseModel:
    """
    Load a Pydantic model from a JSON file with optional numeric cleaning.

    Args:
        model_class (type[BaseModel]): The Pydantic model class to instantiate
        file_path (str): Path to JSON file
        clean_numerics (bool): If True, clean numeric values before validation (default True)
        threshold (float): Values below this are set to 0 (default 1e-10)
        decimals (int): Number of decimal places to round to (default 8)

    Returns:
        BaseModel: Instance of the model class populated from JSON

    Examples:
        >>> class TestModel(BaseModel):
        ...     a: int
        ...     b: str
        ...
        >>> model = load_model_from_json(TestModel, "test.json")
        >>> print(model.a, model.b)
        1 hello

        >>> from onshape_robotics_toolkit.models import Assembly
        >>> assembly = load_model_from_json(Assembly, "assembly.json")
        >>> assembly = load_model_from_json(Assembly, "assembly.json", decimals=5)
    """
    with open(file_path) as file:
        data = json.load(file)
        if clean_numerics:
            data = clean_json_numerics(data, threshold, decimals)
        return model_class.model_validate(data)

make_unique_keys(keys)

Make a list of keys unique by appending a number to duplicate keys and return a mapping of unique keys to their original indices.

Parameters:

Name Type Description Default
keys list[str]

List of keys.

 required

Returns:

Type Description
dict[str, int]

A dictionary mapping unique keys to their original indices.

Examples:

>>> make_unique_keys(["a", "b", "a", "a"])
{"a": 0, "b": 1, "a-1": 2, "a-2": 3}
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def make_unique_keys(keys: list[str]) -> dict[str, int]:
    """
    Make a list of keys unique by appending a number to duplicate keys and
    return a mapping of unique keys to their original indices.

    Args:
        keys: List of keys.

    Returns:
        A dictionary mapping unique keys to their original indices.

    Examples:
        >>> make_unique_keys(["a", "b", "a", "a"])
        {"a": 0, "b": 1, "a-1": 2, "a-2": 3}
    """
    unique_key_map = {}
    key_count: dict[str, int] = {}

    for index, key in enumerate(keys):
        if key in key_count:
            key_count[key] += 1
            unique_key = f"{key}-{key_count[key]}"
        else:
            key_count[key] = 0
            unique_key = key

        unique_key_map[unique_key] = index

    return unique_key_map

make_unique_name(name, existing_names)

Make a name unique by appending a number to the name if it already exists in a set.

Parameters:

Name Type Description Default
name str

Name to make unique.

 required
existing_names set[str]

Set of existing names.

 required

Returns:

Type Description
str

A unique name.

Examples:

>>> make_unique_name("name", {"name"})
"name-1"
>>> make_unique_name("name", {"name", "name-1"})
"name-2"
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def make_unique_name(name: str, existing_names: set[str]) -> str:
    """
    Make a name unique by appending a number to the name if it already exists in a set.

    Args:
        name: Name to make unique.
        existing_names: Set of existing names.

    Returns:
        A unique name.

    Examples:
        >>> make_unique_name("name", {"name"})
        "name-1"
        >>> make_unique_name("name", {"name", "name-1"})
        "name-2"
    """
    if name not in existing_names:
        return name

    count = 1
    while f"{name}-{count}" in existing_names:
        count += 1

    return f"{name}-{count}"

parse_onshape_expression(expr)

Parse an Onshape expression string to a float value.

Handles common units for angles and lengths. Returns None if the expression is None, empty, or cannot be parsed.

Parameters:

Name Type Description Default
expr str | None

Onshape expression string (e.g., "90 deg", "0.5 m", "100 mm")

 required

Returns:

Type Description
float | None

Parsed float value with appropriate unit conversion, or None if invalid
Unit Conversions
  • Angles: "deg" or "°" → radians, "rad" → as-is
  • Length: "m" → as-is, "mm" → /1000, "cm" → /100, "in" → *0.0254
  • No unit: return float as-is

Examples:

>>> parse_onshape_expression("90 deg")
1.5707963267948966
>>> parse_onshape_expression("0.5 m")
0.5
>>> parse_onshape_expression("100 mm")
0.1
>>> parse_onshape_expression("3.14159")
3.14159
>>> parse_onshape_expression(None)
None
>>> parse_onshape_expression("")
None
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def parse_onshape_expression(expr: str | None) -> float | None:
    """
    Parse an Onshape expression string to a float value.

    Handles common units for angles and lengths. Returns None if the expression
    is None, empty, or cannot be parsed.

    Args:
        expr: Onshape expression string (e.g., "90 deg", "0.5 m", "100 mm")

    Returns:
        Parsed float value with appropriate unit conversion, or None if invalid

    Unit Conversions:
        - Angles: "deg" or "°" → radians, "rad" → as-is
        - Length: "m" → as-is, "mm" → /1000, "cm" → /100, "in" → *0.0254
        - No unit: return float as-is

    Examples:
        >>> parse_onshape_expression("90 deg")
        1.5707963267948966
        >>> parse_onshape_expression("0.5 m")
        0.5
        >>> parse_onshape_expression("100 mm")
        0.1
        >>> parse_onshape_expression("3.14159")
        3.14159
        >>> parse_onshape_expression(None)
        None
        >>> parse_onshape_expression("")
        None
    """
    if expr is None or expr.strip() == "":
        return None

    # Clean the expression
    expr = expr.strip()

    # Try to parse as plain float first (no units)
    try:
        return float(expr)
    except ValueError:
        pass

    # Pattern to match number and optional unit
    # Matches: number (int or float), optional whitespace, optional unit
    pattern = r"^([-+]?(?:\d+\.?\d*|\.\d+)(?:[eE][-+]?\d+)?)\s*([a-zA-Z°]+)?$"
    match = re.match(pattern, expr)

    if not match:
        logger.warning(f"Could not parse Onshape expression: '{expr}'")
        return None

    value_str, unit = match.groups()

    try:
        value = float(value_str)
    except ValueError:
        logger.warning(f"Could not convert value to float: '{value_str}' from expression '{expr}'")
        return None

    # No unit provided
    if unit is None or unit == "":
        return value

    # Convert based on unit (case-insensitive)
    unit_lower = unit.lower()

    # Angle conversions
    if unit_lower in ("deg", "degree", "degrees", "°"):
        return float(np.deg2rad(value))
    elif unit_lower in ("rad", "radian", "radians") or unit_lower in ("m", "meter", "meters"):
        return value
    elif unit_lower in ("mm", "millimeter", "millimeters"):
        return value / 1000.0
    elif unit_lower in ("cm", "centimeter", "centimeters"):
        return value / 100.0
    elif unit_lower in ("in", "inch", "inches"):
        return value * 0.0254
    elif unit_lower in ("ft", "foot", "feet"):
        return value * 0.3048

    else:
        logger.warning(f"Unknown unit '{unit}' in expression '{expr}', returning raw value")
        return value

print_dict(d, indent=0)

Print a dictionary with indentation for nested dictionaries

Parameters:

Name Type Description Default
d dict

Dictionary to print

 required
indent int

Number of tabs to indent

 0

Returns:

Type Description
None

None

Examples:

>>> print_dict({"a": 1, "b": {"c": 2}})
a
    1
b
    c
        2
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def print_dict(d: dict, indent: int = 0) -> None:
    """
    Print a dictionary with indentation for nested dictionaries

    Args:
        d (dict): Dictionary to print
        indent (int): Number of tabs to indent

    Returns:
        None

    Examples:
        >>> print_dict({"a": 1, "b": {"c": 2}})
        a
            1
        b
            c
                2
    """

    for key, value in d.items():
        print()
        print("\t" * indent + str(key))
        if isinstance(value, dict):
            print_dict(value, indent + 1)
        else:
            print("\t" * (indent + 1) + str(value))

print_tf(tf)

Print a 4x4 transformation matrix in a readable format

Parameters:

Name Type Description Default
tf ndarray

4x4 transformation matrix

 required

Returns:

Type Description
None

None

Examples:

>>> import numpy as np
>>> tf = np.array([[1, 0, 0, 1],
...                [0, 1, 0, 2],
...                [0, 0, 1, 3],
...                [0, 0, 0, 1]])
>>> print_tf(tf)
[[1.         0.         0.         1.        ]
 [0.         1.         0.         2.        ]
 [0.         0.         1.         3.        ]
 [0.         0.         0.         1.        ]]
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def print_tf(tf: np.ndarray) -> None:
    """
    Print a 4x4 transformation matrix in a readable format

    Args:
        tf (np.ndarray): 4x4 transformation matrix

    Returns:
        None

    Examples:
        >>> import numpy as np
        >>> tf = np.array([[1, 0, 0, 1],
        ...                [0, 1, 0, 2],
        ...                [0, 0, 1, 3],
        ...                [0, 0, 0, 1]])
        >>> print_tf(tf)
        [[1.         0.         0.         1.        ]
         [0.         1.         0.         2.        ]
         [0.         0.         1.         3.        ]
         [0.         0.         0.         1.        ]]
    """
    if tf.shape != (4, 4):
        raise ValueError("Input must be a 4x4 matrix")
    with np.printoptions(precision=8, suppress=True):
        print(tf)

save_model_as_json(model, file_path, indent=4, clean_numerics=True, threshold=1e-10, decimals=8)

Save a Pydantic model as a JSON file with optional numeric cleaning.

Parameters:

Name Type Description Default
model BaseModel

Pydantic model to save

 required
file_path str

File path to save JSON file

 required
indent int

JSON indentation level

 4
clean_numerics bool

If True, clean numeric values (default True)

 True
threshold float

Values below this are set to 0 (default 1e-10)

 1e-10
decimals int

Number of decimal places to round to (default 8)

 8

Returns:

Type Description
None

None

Examples:

>>> class TestModel(BaseModel):
...     a: int
...     b: str
...
>>> save_model_as_json(TestModel(a=1, b="hello"), "test.json")
>>> save_model_as_json(model, "test.json", decimals=5, threshold=1e-8)
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def save_model_as_json(
    model: BaseModel,
    file_path: str,
    indent: int = 4,
    clean_numerics: bool = True,
    threshold: float = 1e-10,
    decimals: int = 8,
) -> None:
    """
    Save a Pydantic model as a JSON file with optional numeric cleaning.

    Args:
        model (BaseModel): Pydantic model to save
        file_path (str): File path to save JSON file
        indent (int): JSON indentation level
        clean_numerics (bool): If True, clean numeric values (default True)
        threshold (float): Values below this are set to 0 (default 1e-10)
        decimals (int): Number of decimal places to round to (default 8)

    Returns:
        None

    Examples:
        >>> class TestModel(BaseModel):
        ...     a: int
        ...     b: str
        ...
        >>> save_model_as_json(TestModel(a=1, b="hello"), "test.json")
        >>> save_model_as_json(model, "test.json", decimals=5, threshold=1e-8)
    """
    with open(file_path, "w") as file:
        encoder = CustomJSONEncoder(
            indent=indent, clean_numerics=clean_numerics, threshold=threshold, decimals=decimals
        )
        file.write(encoder.encode(model.model_dump()))

setup_console_logging(level='INFO', format_string=None, colorize=True)

Add a console (stderr) logging handler.

Parameters:

Name Type Description Default
level str

Minimum log level to display (DEBUG, INFO, WARNING, ERROR, CRITICAL)

 'INFO'
format_string str | None

Custom format string. If None, uses DEFAULT_CONSOLE_FORMAT

 None
colorize bool

Whether to colorize the output

 True

Returns:

Type Description
int

Handler ID that can be used with logger.remove() if needed
Example

from onshape_robotics_toolkit.utilities import setup_console_logging setup_console_logging(level="DEBUG")

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def setup_console_logging(
    level: str = "INFO",
    format_string: str | None = None,
    colorize: bool = True,
) -> int:
    """Add a console (stderr) logging handler.

    Args:
        level: Minimum log level to display (DEBUG, INFO, WARNING, ERROR, CRITICAL)
        format_string: Custom format string. If None, uses DEFAULT_CONSOLE_FORMAT
        colorize: Whether to colorize the output

    Returns:
        Handler ID that can be used with logger.remove() if needed

    Example:
        >>> from onshape_robotics_toolkit.utilities import setup_console_logging
        >>> setup_console_logging(level="DEBUG")
    """
    import sys

    fmt = format_string if format_string is not None else DEFAULT_CONSOLE_FORMAT

    handler_id = logger.add(
        sys.stderr,
        format=fmt,
        level=level,
        colorize=colorize,
    )
    return handler_id

setup_default_logging(console_level='INFO', file_level='DEBUG', file_path='onshape_toolkit.log', clear_existing_handlers=True, delay_file_creation=False)

Configure logging with sensible defaults: console at INFO + file at DEBUG.

This is the recommended way to set up logging for most users. It provides: - Colored console output at INFO level or higher - Detailed file logging at DEBUG level or higher - Automatic log rotation (10 MB) and retention (7 days) - Compressed archives of rotated logs

Parameters:

Name Type Description Default
console_level str

Minimum level for console output (default: "INFO")

 'INFO'
file_level str

Minimum level for file output (default: "DEBUG")

 'DEBUG'
file_path str

Path to the log file (default: "onshape_toolkit.log")

 'onshape_toolkit.log'
clear_existing_handlers bool

Whether to remove existing handlers first (default: True)

 True

Returns:

Type Description
tuple[int, int]

Tuple of (console_handler_id, file_handler_id)
Example

from onshape_robotics_toolkit.utilities import setup_default_logging setup_default_logging() # Use all defaults

Or customize:

setup_default_logging(console_level="DEBUG", file_path="my_robot.log")

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def setup_default_logging(
    console_level: str = "INFO",
    file_level: str = "DEBUG",
    file_path: str = "onshape_toolkit.log",
    clear_existing_handlers: bool = True,
    delay_file_creation: bool = False,
) -> tuple[int, int]:
    """Configure logging with sensible defaults: console at INFO + file at DEBUG.

    This is the recommended way to set up logging for most users. It provides:
    - Colored console output at INFO level or higher
    - Detailed file logging at DEBUG level or higher
    - Automatic log rotation (10 MB) and retention (7 days)
    - Compressed archives of rotated logs

    Args:
        console_level: Minimum level for console output (default: "INFO")
        file_level: Minimum level for file output (default: "DEBUG")
        file_path: Path to the log file (default: "onshape_toolkit.log")
        clear_existing_handlers: Whether to remove existing handlers first (default: True)

    Returns:
        Tuple of (console_handler_id, file_handler_id)

    Example:
        >>> from onshape_robotics_toolkit.utilities import setup_default_logging
        >>> setup_default_logging()  # Use all defaults
        >>> # Or customize:
        >>> setup_default_logging(console_level="DEBUG", file_path="my_robot.log")
    """
    if clear_existing_handlers:
        logger.remove()

    console_id = setup_console_logging(level=console_level, colorize=True)
    file_id = setup_file_logging(file_path=file_path, level=file_level, delay=delay_file_creation)

    _record_logging_config(
        mode="default",
        console_level=console_level,
        file_level=file_level,
        file_path=file_path,
        clear_existing_handlers=clear_existing_handlers,
        delay_file_creation=delay_file_creation,
    )

    return console_id, file_id

setup_file_logging(file_path='onshape_toolkit.log', level='DEBUG', format_string=None, rotation='10 MB', retention='7 days', compression='zip', enqueue=True, delay=False)

Add a file logging handler with rotation and compression.

Parameters:

Name Type Description Default
file_path str

Path to the log file

 'onshape_toolkit.log'
level str

Minimum log level to write (DEBUG, INFO, WARNING, ERROR, CRITICAL)

 'DEBUG'
format_string str | None

Custom format string. If None, uses DEFAULT_FILE_FORMAT

 None
rotation str

When to rotate the log file (e.g., "10 MB", "1 day", "12:00")

 '10 MB'
retention str

How long to keep old log files (e.g., "7 days", "10 files")

 '7 days'
compression str

Compression format for rotated files ("zip", "gz", "bz2", or None)

 'zip'
enqueue bool

Whether to use thread-safe logging (recommended)

 True

Returns:

Type Description
int

Handler ID that can be used with logger.remove() if needed
Example

from onshape_robotics_toolkit.utilities import setup_file_logging setup_file_logging("my_robot.log", level="DEBUG", rotation="50 MB")

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def setup_file_logging(
    file_path: str = "onshape_toolkit.log",
    level: str = "DEBUG",
    format_string: str | None = None,
    rotation: str = "10 MB",
    retention: str = "7 days",
    compression: str = "zip",
    enqueue: bool = True,
    delay: bool = False,
) -> int:
    """Add a file logging handler with rotation and compression.

    Args:
        file_path: Path to the log file
        level: Minimum log level to write (DEBUG, INFO, WARNING, ERROR, CRITICAL)
        format_string: Custom format string. If None, uses DEFAULT_FILE_FORMAT
        rotation: When to rotate the log file (e.g., "10 MB", "1 day", "12:00")
        retention: How long to keep old log files (e.g., "7 days", "10 files")
        compression: Compression format for rotated files ("zip", "gz", "bz2", or None)
        enqueue: Whether to use thread-safe logging (recommended)

    Returns:
        Handler ID that can be used with logger.remove() if needed

    Example:
        >>> from onshape_robotics_toolkit.utilities import setup_file_logging
        >>> setup_file_logging("my_robot.log", level="DEBUG", rotation="50 MB")
    """
    fmt = format_string if format_string is not None else DEFAULT_FILE_FORMAT

    handler_id = logger.add(
        file_path,
        format=fmt,
        level=level,
        rotation=rotation,
        retention=retention,
        compression=compression,
        enqueue=enqueue,
        delay=delay,
    )
    return handler_id

setup_minimal_logging(level='INFO')

Configure minimal console-only logging without file output.

Useful for quick scripts or when you don't want log files.

Parameters:

Name Type Description Default
level str

Minimum log level to display (default: "INFO")

 'INFO'

Returns:

Type Description
int

Handler ID that can be used with logger.remove() if needed
Example

from onshape_robotics_toolkit.utilities import setup_minimal_logging setup_minimal_logging(level="WARNING")

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def setup_minimal_logging(level: str = "INFO") -> int:
    """Configure minimal console-only logging without file output.

    Useful for quick scripts or when you don't want log files.

    Args:
        level: Minimum log level to display (default: "INFO")

    Returns:
        Handler ID that can be used with logger.remove() if needed

    Example:
        >>> from onshape_robotics_toolkit.utilities import setup_minimal_logging
        >>> setup_minimal_logging(level="WARNING")
    """
    logger.remove()
    handler_id = setup_console_logging(level=level, format_string=MINIMAL_CONSOLE_FORMAT)
    _record_logging_config(mode="minimal", console_level=level)
    return handler_id

setup_quiet_logging(file_path='onshape_toolkit.log', level='DEBUG')

Configure file-only logging with no console output.

Useful for background tasks or automated scripts where console output would be distracting.

Parameters:

Name Type Description Default
file_path str

Path to the log file (default: "onshape_toolkit.log")

 'onshape_toolkit.log'
level str

Minimum log level to write (default: "DEBUG")

 'DEBUG'

Returns:

Type Description
int

Handler ID that can be used with logger.remove() if needed
Example

from onshape_robotics_toolkit.utilities import setup_quiet_logging setup_quiet_logging("background_task.log")

Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def setup_quiet_logging(file_path: str = "onshape_toolkit.log", level: str = "DEBUG") -> int:
    """Configure file-only logging with no console output.

    Useful for background tasks or automated scripts where console output
    would be distracting.

    Args:
        file_path: Path to the log file (default: "onshape_toolkit.log")
        level: Minimum log level to write (default: "DEBUG")

    Returns:
        Handler ID that can be used with logger.remove() if needed

    Example:
        >>> from onshape_robotics_toolkit.utilities import setup_quiet_logging
        >>> setup_quiet_logging("background_task.log")
    """
    logger.remove()
    handler_id = setup_file_logging(file_path=file_path, level=level)
    _record_logging_config(
        mode="quiet",
        file_path=file_path,
        file_level=level,
        clear_existing_handlers=True,
    )
    return handler_id

xml_escape(unescaped)

Escape XML characters in a string

Parameters:

Name Type Description Default
unescaped str

Unescaped string

 required

Returns:

Name Type Description
str str

Escaped string

Examples:

>>> xml_escape("hello 'world' "world"")
"hello &apos;world&apos; &quot;world&quot;"

>>> xml_escape("hello <world>")
"hello &lt;world&gt;"
Source code in onshape_robotics_toolkit\utilities\helpers.py 
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def xml_escape(unescaped: str) -> str:
    """
    Escape XML characters in a string

    Args:
        unescaped (str): Unescaped string

    Returns:
        str: Escaped string

    Examples:
        >>> xml_escape("hello 'world' \"world\"")
        "hello &apos;world&apos; &quot;world&quot;"

        >>> xml_escape("hello <world>")
        "hello &lt;world&gt;"
    """

    return escape(unescaped, entities={"'": "&apos;", '"': "&quot;"})