Matlab

load_array(file_name, var_names)

This is used to load info from v7.3 or later matlab files. It is also good at dealing with complicated matlab cell arrays which are loaded as numpy object arrays.

If var_names is str, one value is returned, otherwise tuple of all values requested is returned.

Parameters:

Name	Type	Description	Default
file_name	str	Path of MATLAB file.	required
var_names	Union[str, List[str]]	str [n_vars]. Names of variables desired.	required

Returns:

Type	Description
Union[tuple, np.ndarray]	Tuple of n_vars numpy arrays.

Source code in coppafish/utils/matlab.py

def load_array(file_name: str, var_names: Union[str, List[str]]) -> Union[tuple, np.ndarray]:
    """
    This is used to load info from v7.3 or later matlab files.
    It is also good at dealing with complicated matlab cell arrays which are loaded as numpy object arrays.

    If `var_names` is `str`, one value is returned, otherwise `tuple` of all values requested is returned.

    Args:
        file_name: Path of MATLAB file.
        var_names: `str [n_vars]`.
            Names of variables desired.

    Returns:
        `Tuple` of `n_vars` numpy arrays.
    """
    f = h5py.File(file_name)
    if not isinstance(var_names, list):
        output = np.array(f[var_names]).transpose()
    else:
        output = []
        for var_name in var_names:
            output.append(np.array(f[var_name]).transpose())
        output = tuple(output)
    return output

load_cell(file_name, var_name)

If cell is M x N, will return list of length M where each entry is another list of length N and each element of this list is a numpy array.

Parameters:

Name	Type	Description	Default
file_name	str	Path of MATLAB file.	required
var_name	str	Names of variable in MATLAB file.	required

Returns:

Type	Description
list	MATLAB cell var_name as a list of numpy arrays.

Source code in coppafish/utils/matlab.py

def load_cell(file_name: str, var_name: str) -> list:
    """
    If cell is `M x N`, will return list of length `M` where each entry is another list of length `N`
    and each element of this list is a numpy array.

    Args:
        file_name: Path of MATLAB file.
        var_name: Names of variable in MATLAB file.

    Returns:
        MATLAB cell `var_name` as a list of numpy arrays.
    """
    # MAYBE CHANGE THIS TO OBJECT NUMPY ARRAY
    f = h5py.File(file_name)
    data = []
    for column in np.transpose(f[var_name]):
        row_data = []
        for row_number in range(len(column)):
            row_data.append(np.array(f[column[row_number]]).transpose())
        data.append(row_data)
    return data

load_v_less_7_3(file_name, var_names)

This is used to load info from earlier than v7.3 matlab files. It is also good at dealing with complicated matlab cell arrays which are loaded as numpy object arrays.

If var_names is str, one value is returned, otherwise tuple of all values requested is returned.

Parameters:

Name	Type	Description	Default
file_name	str	Path of MATLAB file.	required
var_names	Union[str, List[str]]	str [n_vars]. Names of variables desired.	required

Returns:

Type	Description
Union[tuple, np.ndarray]	Tuple of n_vars numpy arrays.

Source code in coppafish/utils/matlab.py

def load_v_less_7_3(file_name: str, var_names: Union[str, List[str]]) -> Union[tuple, np.ndarray]:
    """
    This is used to load info from earlier than v7.3  matlab files.
    It is also good at dealing with complicated matlab cell arrays which are loaded as numpy object arrays.

    If `var_names` is `str`, one value is returned, otherwise tuple of all values requested is returned.

    Args:
        file_name: Path of MATLAB file.
        var_names: `str [n_vars]`.
            Names of variables desired.

    Returns:
        `Tuple` of `n_vars` numpy arrays.
    """
    f = io.loadmat(file_name)
    if not isinstance(var_names, list):
        output = f[var_names]
    else:
        output = []
        for var_name in var_names:
            output.append(f[var_name])
        output = tuple(output)
    return output

save_nb_results(nb, file_name, score_thresh_ref_spots=0.15, score_thresh_omp=0.15)

Saves important information in notebook as a .mat file so can load in MATLAB and plot using python_testing/iss_object_from_python.m scripts

Parameters:

Name	Type	Description	Default
nb	Notebook	Notebook containing at least call_spots page.	required
file_name	str	Path to save matlab version of Notebook.	required
score_thresh_ref_spots	float	Only ref_round spots with score exceeding this will be saved.	0.15
score_thresh_omp	float	Only omp spots with score exceeding this will be saved.	0.15

Source code in coppafish/utils/matlab.py

def save_nb_results(nb: Notebook, file_name: str, score_thresh_ref_spots: float = 0.15, score_thresh_omp: float = 0.15):
    """
    Saves important information in notebook as a .mat file so can load in MATLAB and plot
    using python_testing/iss_object_from_python.m scripts

    Args:
        nb: Notebook containing at least `call_spots` page.
        file_name: Path to save matlab version of Notebook.
        score_thresh_ref_spots: Only `ref_round` spots with score exceeding this will be saved.
        score_thresh_omp: Only `omp` spots with score exceeding this will be saved.

    """
    mdic = {}
    if nb.has_page('file_names'):
        mdic['tile_file_names'] = nb.file_names.tile
    if nb.has_page('stitch'):
        mdic['tile_origin'] = nb.stitch.tile_origin
    if nb.has_page('register'):
        mdic['transform'] = nb.register.transform

    if nb.has_page('call_spots'):
        call_spots_dict = nb.call_spots.to_serial_dict()
        call_spots_dict = {k: v for k, v in call_spots_dict.items() if not '___' in k}
        del call_spots_dict['PAGEINFO']
        mdic.update(call_spots_dict)

    if nb.has_page('ref_spots'):
        ref_spots_dict = nb.ref_spots.to_serial_dict()
        # Give 'ref_spots' prefix to key names as same variables in omp page.
        ref_spots_dict = {ref_spots_dict['PAGEINFO'] + '_' + k: v for k, v in ref_spots_dict.items() if not '___' in k}
        del ref_spots_dict['ref_spots_PAGEINFO']
        ref_spots_dict = update_dict(nb.ref_spots, nb, ref_spots_dict, score_thresh_ref_spots)
        mdic.update(ref_spots_dict)

    if nb.has_page('omp'):
        omp_dict = nb.omp.to_serial_dict()
        omp_dict = {omp_dict['PAGEINFO'] + '_' + k:v for k,v in omp_dict.items() if not '___' in k}
        del omp_dict['omp_PAGEINFO']
        omp_dict = update_dict(nb.omp, nb, omp_dict, score_thresh_omp)
        mdic.update(omp_dict)
    for k, v in mdic.items():
        if v is None:
            mdic[k] = []  # Can't save None values
    io.savemat(file_name, mdic)

update_dict(nbp, nb, spots_info_dict, score_thresh)

Used in save_nb_results to reduced amount of spots saved. Only spots with score > score_thresh kept.

Parameters:

Name	Type	Description	Default
nbp	NotebookPage	Either omp or ref_spots NotebookPage.	required
nb	Notebook	Full notebook	required
spots_info_dict	dict	Dictionary produced in save_nb_results containing information to save about each spot.	required
score_thresh	float	All spots with a score above this threshold will be returned.	required

Returns:

Type	Description
dict	spots_info_dict is returned, just including spots for which score > score_thresh.

Source code in coppafish/utils/matlab.py

def update_dict(nbp: NotebookPage, nb: Notebook, spots_info_dict: dict, score_thresh: float) -> dict:
    """
    Used in `save_nb_results` to reduced amount of spots saved. Only spots with `score > score_thresh` kept.

    Args:
        nbp: Either `omp` or `ref_spots` NotebookPage.
        nb: Full notebook
        spots_info_dict: Dictionary produced in `save_nb_results` containing information to save about each spot.
        score_thresh: All spots with a score above this threshold will be returned.

    Returns:
        `spots_info_dict` is returned, just including spots for which `score > score_thresh`.

    """
    pf = nbp.name + '_'
    if pf != 'omp_' and pf != 'ref_spots_':
        raise ValueError("Wrong page given, should be 'omp' or 'ref_spots'")
    nbp.finalized = False
    score_thresh_old = nbp.score_thresh
    del nbp.score_thresh
    nbp.score_thresh = score_thresh
    if pf == 'omp_':
        keep = quality_threshold(nb, 'omp')
    else:
        keep = quality_threshold(nb, 'ref')
    del nbp.score_thresh
    nbp.score_thresh = score_thresh_old
    nbp.finalized = True
    for var in ['local_yxz', 'tile', 'colors', 'intensity', 'background_coef', 'gene_no']:
        spots_info_dict[pf + var] = spots_info_dict[pf + var][keep]
    if pf == 'ref_spots_':
        del spots_info_dict[pf + 'background_coef']
        for var in ['isolated', 'score', 'score_diff']:
            spots_info_dict[pf + var] = spots_info_dict[pf + var][keep]
    if pf == 'omp_':
        for var in ['shape_spot_local_yxz', 'shape_spot_gene_no', 'spot_shape_float']:
            del spots_info_dict[pf + var]
        for var in ['coef', 'n_neighbours_pos', 'n_neighbours_neg']:
            spots_info_dict[pf + var] = spots_info_dict[pf + var][keep]
    return spots_info_dict