io

load_adjusters(path, mirror)

Get nominal adjuster locations from file.

Parameters:

Name	Type	Description	Default
path	str	Path to the data file.	required
mirror	str	The mirror that these points belong to. Should be either: 'primary' or 'secondary'.	'primary'

Returns:

Name	Type	Description
adjusters	dict[tuple[int, int], NDArray[float64]]	Nominal adjuster locations. This is indexed by a (row, col) tuple. Each entry is (5, 3) array where each row is an adjuster.

Source code in lat_alignment/io.py

def load_adjusters(
    path: str, mirror: str
) -> dict[tuple[int, int], NDArray[np.float64]]:
    """
    Get nominal adjuster locations from file.

    Parameters
    ----------
    path : str
        Path to the data file.
    mirror : str, default: 'primary'
        The mirror that these points belong to.
        Should be either: 'primary' or 'secondary'.

    Returns
    -------
    adjusters : dict[tuple[int, int], NDArray[np.float64]]
        Nominal adjuster locations.
        This is indexed by a (row, col) tuple.
        Each entry is `(5, 3)` array where each row is an adjuster.
    """
    if mirror not in ["primary", "secondary"]:
        raise ValueError(f"Invalid mirror: {mirror}")

    def _transform(coords):
        coords = np.atleast_2d(coords)
        coords -= np.array([120, 0, 0])  # cancel out shift
        return coord_transform(coords, "va_global", f"opt_{mirror}")

    # TODO: cleaner transform call
    adjusters = defaultdict(list)
    c_points = np.genfromtxt(path, dtype=str)
    for point in c_points:
        row = point[0][6]
        col = point[0][7]
        adjusters[(row, col)] += [_transform(np.array(point[2:], dtype=np.float64))[0]]
    adjusters = {rc: np.vstack(pts) for rc, pts in adjusters.items()}

    return adjusters

load_corners(path)

Get panel corners from file.

Parameters:

Name	Type	Description	Default
path	str	Path to the data file.	required

Returns:

Name	Type	Description
corners	dict[tuple[int, int], ndarray[float64]]	The corners. This is indexed by a (row, col) tuple. Each entry is (4, 3) array where each row is a corner.

Source code in lat_alignment/io.py

def load_corners(path: str) -> dict[tuple[int, int], NDArray[np.float64]]:
    """
    Get panel corners from file.

    Parameters
    ----------
    path : str
        Path to the data file.

    Returns
    -------
    corners : dict[tuple[int, int], ndarray[np.float64]]
        The corners. This is indexed by a (row, col) tuple.
        Each entry is `(4, 3)` array where each row is a corner.
    """
    with open(path) as file:
        corners_raw = yaml.safe_load(file)

    corners = {
        (panel[7], panel[9]): np.vstack(
            [np.array(coord.split(), np.float64) for coord in coords]
        )
        for panel, coords in corners_raw.items()
    }
    return corners

load_data(path, source='photo', **kwargs)

Load a dataset from path.

Parameters:

Name	Type	Description	Default
path	str	The path to the data to load.	required
source	str	The data source. Current valid options are: photo tracker	'photo'
**kwargs		Arguments to pass the relevent loader function. See load_photo and load_tracker for details.	{}

Source code in lat_alignment/io.py

def load_data(path: str, source: str = "photo", **kwargs) -> Dataset:
    """
    Load a dataset from path.

    Parameters
    ----------
    path : str
        The path to the data to load.
    source : str, default: 'photo'
        The data source. Current valid options are:

        * photo
        * tracker
    **kwargs
        Arguments to pass the relevent loader function.
        See `load_photo` and `load_tracker` for details.
    """
    if source == "photo":
        return load_photo(path, **kwargs)
    elif source == "tracker":
        return load_tracker(path, **kwargs)
    raise ValueError("Invalid data source")

load_photo(path, err_thresh=2, doubles_dist=10, plot=True)

Load photogrammetry data. Assuming first column is target names and next three are (x, y , z).

Parameters:

Name	Type	Description	Default
path	str	The path to the photogrammetry data.	required
err_thresh	float	How many times the median photogrammetry error a target need to have to be cut.	2
plot	bool	If True display a scatter plot of targets.	True

Returns:

Name	Type	Description
data	Dataset	The photogrammetry data.

Source code in lat_alignment/io.py

def load_photo(
    path: str, err_thresh: float = 2, doubles_dist: float = 10, plot: bool = True
) -> Dataset:
    """
    Load photogrammetry data.
    Assuming first column is target names and next three are (x, y , z).

    Parameters
    ----------
    path : str
        The path to the photogrammetry data.
    err_thresh : float, default: 2
        How many times the median photogrammetry error
        a target need to have to be cut.
    plot: bool, default: True
        If True display a scatter plot of targets.

    Returns
    -------
    data : Dataset
        The photogrammetry data.
    """
    logger.info("Loading measurement data")
    labels = np.genfromtxt(path, dtype=str, delimiter=",", usecols=(0,))
    coords = np.genfromtxt(path, dtype=np.float64, delimiter=",", usecols=(1, 2, 3))
    errs = np.genfromtxt(path, dtype=np.float64, delimiter=",", usecols=(4, 5, 6))
    msk = (np.char.find(labels, "TARGET") >= 0) + (np.char.find(labels, "CODE") >= 0)

    labels, coords, errs = labels[msk], coords[msk], errs[msk]
    err = np.linalg.norm(errs, axis=-1)
    trg_msk = np.char.find(labels, "TARGET") >= 0
    code_msk = np.char.find(labels, "CODE") >= 0

    err_msk = (err < err_thresh * np.median(err[trg_msk])) + code_msk
    labels, coords, err, errs = (
        labels[err_msk],
        coords[err_msk],
        err[err_msk],
        errs[err_msk],
    )
    logger.info("\t%d good points loaded", len(coords))
    logger.info("\t%d high error points not loaded", np.sum(~err_msk))

    # Lets find and remove doubles
    # Dumb brute force
    trg_msk = np.char.find(labels, "TARGET") >= 0
    edm = make_edm(coords[trg_msk, :2])
    np.fill_diagonal(edm, np.nan)
    to_kill = []
    for i in range(len(edm)):
        if labels[trg_msk][i] in to_kill:
            continue
        imin = np.nanargmin(edm[i])
        if edm[i][imin] > doubles_dist:
            continue
        if err[trg_msk][i] < err[trg_msk][imin]:
            to_kill += [labels[trg_msk][imin]]
        else:
            to_kill += [labels[trg_msk][i]]
    msk = ~np.isin(labels, to_kill)
    logger.info("\tFound and removed %d doubles", len(to_kill))
    labels, coords, err, errs = labels[msk], coords[msk], err[msk], errs[msk]

    if plot:
        fig = plt.figure()
        ax = fig.add_subplot(projection="3d")
        p = ax.scatter(
            coords[:, 0],
            coords[:, 1],
            coords[:, 2],
            marker="x",
            c=err,
            vmax=np.percentile(err, 90),
        )
        fig.colorbar(p)
        plt.show()

    data = {
        label: np.array([coord, err]) for label, coord, err in zip(labels, coords, errs)
    }
    return DatasetPhotogrammetry(data)

load_tracker(path, group_dist=0.02, group_thresh=0.02, err=0.005, calc_sys_err=False, cam_transform_path=None, dist_err=8e-07, ang_err=5e-06)

Load laser tracker data.

Parameters:

Name	Type	Description	Default
path	str	The path to the laser tracker data. The type of data will be infered from the extension.	required
group_dist	float	Distance between points in xy needed to group them for cuts. Only used for .txt files. Set to 0 to disable.	0.02
group_thresh	float	Difference in z between point and the median z for a group to cut at. Only used for .txt files. Set to 0 to disable.	0.02
err	float	The base error to assume for the tracker data. Only used for .txt files.	.005
calc_sys_err	bool	It True calculate the systematic error based on provided tracker specs. Only used for .txt files.	False
cam_transform_path	Optional[str]	Alignment matrix exported from CAM2. Used when calculating systematic error. If not provided we assume the data is the the FARO's internal coordinates. Only used for .txt files.	None
dist_err	float	The systematic error as a function of distance in mm/mm. Only used for .txt files.	8e-7
ang_err	float	The systematic error as a function of angle in mm/mm. Only used for .txt files.	8e-7

Returns:

Name	Type	Description
data	Dataset	The tracker data. For txt or csv files this will be the base Dataset class. For yaml files this will be a DatasetReference.

Source code in lat_alignment/io.py

def load_tracker(
    path: str,
    group_dist=0.02,
    group_thresh=0.02,
    err=0.005,
    calc_sys_err: bool = False,
    cam_transform_path: Optional[str] = None,
    dist_err: float = 8e-7,
    ang_err: float = 5e-6,
) -> Dataset:
    """
    Load laser tracker data.

    Parameters
    ----------
    path : str
        The path to the laser tracker data.
        The type of data will be infered from the extension.
    group_dist : float, default: 0.02
        Distance between points in xy needed to group them for cuts.
        Only used for `.txt` files.
        Set to 0 to disable.
    group_thresh : float, default: 0.02
        Difference in z between point and the median z for a group to cut at.
        Only used for `.txt` files.
        Set to 0 to disable.
    err : float, default: .005
        The base error to assume for the tracker data.
        Only used for `.txt` files.
    calc_sys_err : bool, default: False
        It `True` calculate the systematic error based on provided tracker specs.
        Only used for `.txt` files.
    cam_transform_path : Optional[str], default: None
        Alignment matrix exported from CAM2.
        Used when calculating systematic error.
        If not provided we assume the data is the the FARO's internal coordinates.
        Only used for `.txt` files.
    dist_err : float, default 8e-7
        The systematic error as a function of distance in mm/mm.
        Only used for `.txt` files.
    ang_err : float, default 8e-7
        The systematic error as a function of angle in mm/mm.
        Only used for `.txt` files.

    Returns
    -------
    data : Dataset
        The tracker data.
        For txt or csv files this will be the base `Dataset` class.
        For yaml files this will be a `DatasetReference`.
    """
    ext = os.path.splitext(path)[1]
    if ext == ".yaml":
        return _load_tracker_yaml(path)
    elif ext == ".txt":
        return _load_tracker_txt(
            path,
            group_dist,
            group_thresh,
            err,
            calc_sys_err,
            cam_transform_path,
            dist_err,
            ang_err,
        )
    elif ext == ".csv":
        return _load_tracker_csv(path)
    raise ValueError(f"Invalid tracker data with extension {ext}")