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nblast_funcs

Implements version 2 of the NBLAST algorithm.

Please note that some properties are computed on initialization and changing parameters (e.g. use_alpha) at a later stage will mess things up!

PARAMETER DESCRIPTION
use_alpha 
        Whether or not to use alpha values for the scoring.
        If True, the dotproduct of nearest neighbor vectors will
        be scaled by `sqrt(alpha1 * alpha2)`.

TYPE: bool DEFAULT: False

normalized 
        If True, will normalize scores by the best possible score
        (i.e. self-self) of the query neuron.

TYPE: bool DEFAULT: True

smat 
        How to convert the point match pairs into an NBLAST score,
        usually by a lookup table:
         - if 'auto' (default), will use the "official" NBLAST scoring
           matrices based on FCWB data. Same behaviour as in R's
           nat.nblast implementation.
         - if `smat='v1'` it uses the analytic formulation of the
           NBLAST scoring from Kohl et. al (2013). You can adjust parameter
           `sigma_scaling` (default to 10) using `smat_kwargs`.
         - DataFrames will be used to build a `Lookup2d`
         - if `Callable` given, it passes distance and dot products as
           first and second argument respectively
         - if `smat=None` the scores will be generated as the
           product of the distances and the dotproduct of the vectors
           of nearest-neighbor pairs

TYPE: navis.nbl.smat.Lookup2d | pd.DataFrame | str | Callable DEFAULT: 'auto'

smat_kwargs 
        functions. For example: `smat_kwargs["sigma_scoring"] = 10`.

DEFAULT: dict()

limit_dist 
        Sets the max distance for the nearest neighbor search
        (`distance_upper_bound`). Typically this should be the
        highest distance considered by the scoring function. If
        "auto", will extract that value from the first axis of the
        scoring matrix.

TYPE: float | "auto" | None DEFAULT: None

progress 
        If True, will show a progress bar.

TYPE: bool DEFAULT: True

Source code in navis/nbl/nblast_funcs.py 
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class NBlaster(Blaster):
    """Implements version 2 of the NBLAST algorithm.

    Please note that some properties are computed on initialization and
    changing parameters (e.g. `use_alpha`) at a later stage will mess things
    up!

    Parameters
    ----------
    use_alpha :     bool
                    Whether or not to use alpha values for the scoring.
                    If True, the dotproduct of nearest neighbor vectors will
                    be scaled by `sqrt(alpha1 * alpha2)`.
    normalized :    bool
                    If True, will normalize scores by the best possible score
                    (i.e. self-self) of the query neuron.
    smat :          navis.nbl.smat.Lookup2d | pd.DataFrame | str | Callable
                    How to convert the point match pairs into an NBLAST score,
                    usually by a lookup table:
                     - if 'auto' (default), will use the "official" NBLAST scoring
                       matrices based on FCWB data. Same behaviour as in R's
                       nat.nblast implementation.
                     - if `smat='v1'` it uses the analytic formulation of the
                       NBLAST scoring from Kohl et. al (2013). You can adjust parameter
                       `sigma_scaling` (default to 10) using `smat_kwargs`.
                     - DataFrames will be used to build a `Lookup2d`
                     - if `Callable` given, it passes distance and dot products as
                       first and second argument respectively
                     - if `smat=None` the scores will be generated as the
                       product of the distances and the dotproduct of the vectors
                       of nearest-neighbor pairs
    smat_kwargs:    Dictionary with additional parameters passed to scoring
                    functions. For example: `smat_kwargs["sigma_scoring"] = 10`.
    limit_dist :    float | "auto" | None
                    Sets the max distance for the nearest neighbor search
                    (`distance_upper_bound`). Typically this should be the
                    highest distance considered by the scoring function. If
                    "auto", will extract that value from the first axis of the
                    scoring matrix.
    progress :      bool
                    If True, will show a progress bar.

    """

    def __init__(self, use_alpha=False, normalized=True, smat='auto',
                 limit_dist=None, approx_nn=False, dtype=np.float64,
                 progress=True, smat_kwargs=dict()):
        """Initialize class."""
        super().__init__(progress=progress, dtype=dtype)
        self.use_alpha = use_alpha
        self.normalized = normalized
        self.approx_nn = approx_nn
        self.desc = "NBlasting"

        if smat is None:
            self.score_fn = operator.mul
        elif isinstance(smat, pd.DataFrame):
            self.score_fn = Lookup2d.from_dataframe(smat)
        elif isinstance(smat, str) and smat == 'auto':
            self.score_fn = smat_fcwb(self.use_alpha)
        elif isinstance(smat, str) and smat == 'v1':
            self.score_fn = partial(
                _nblast_v1_scoring, sigma_scoring = smat_kwargs.get('sigma_scoring', 10)
            )
        else:
            self.score_fn = smat

        if limit_dist == "auto":
            try:
                if self.score_fn.axes[0].boundaries[-1] != np.inf:
                    self.distance_upper_bound = self.score_fn.axes[0].boundaries[-1]
                else:
                    # If the right boundary is open (i.e. infinity), we will use
                    # the second highest boundary plus a 5% offset
                    self.distance_upper_bound = self.score_fn.axes[0].boundaries[-2] * 1.05
            except AttributeError:
                logger.warning("Could not infer distance upper bound from scoring function")
                self.distance_upper_bound = None
        else:
            self.distance_upper_bound = limit_dist

    def append(self, dotprops: Dotprops, self_hit: Optional[float] = None) -> NestedIndices:
        """Append dotprops.

        Returns the numerical index appended dotprops.
        If dotprops is a (possibly nested) sequence of dotprops,
        return a (possibly nested) list of indices.

        Note that `self_hit` is ignored (and hence calculated from scratch)
        when `dotprops` is a nested list of dotprops.
        """
        if isinstance(dotprops, Dotprops):
            return self._append_dotprops(dotprops, self_hit=self_hit)

        try:
            return [self.append(n) for n in dotprops]
        except TypeError:  # i.e. not iterable
            raise ValueError(f"Expected Dotprops or iterable thereof; got {type(dotprops)}")

    def _append_dotprops(self, dotprops: Dotprops, self_hit: Optional[float] = None) -> int:
        next_id = len(self)
        self.neurons.append(dotprops)
        self.ids.append(dotprops.id)
        # Calculate score for self hit
        if not self_hit:
            self.self_hits.append(self.calc_self_hit(dotprops))
        else:
            self.self_hits.append(self_hit)
        return next_id

    def calc_self_hit(self, dotprops):
        """Non-normalized value for self hit."""
        if not self.use_alpha:
            return len(dotprops.points) * self.score_fn(0, 1.0)
        else:
            dists = np.repeat(0, len(dotprops.points))
            alpha = dotprops.alpha * dotprops.alpha
            dots = np.repeat(1, len(dotprops.points)) * np.sqrt(alpha)
            return self.score_fn(dists, dots).sum()

    def single_query_target(self, q_idx: int, t_idx: int, scores='forward'):
        """Query single target against single target."""
        # Take a short-cut if this is a self-self comparison
        if q_idx == t_idx:
            if self.normalized:
                return 1
            return self.self_hits[q_idx]

        # Run nearest-neighbor search for query against target
        data = self.neurons[q_idx].dist_dots(self.neurons[t_idx],
                                             alpha=self.use_alpha,
                                             # eps=0.1 means we accept 10% inaccuracy
                                             eps=.1 if self.approx_nn else 0,
                                             distance_upper_bound=self.distance_upper_bound)
        if self.use_alpha:
            dists, dots, alpha = data
            dots *= np.sqrt(alpha)
        else:
            dists, dots = data

        scr = self.score_fn(dists, dots).sum()

        # Normalize against best hit
        if self.normalized:
            scr /= self.self_hits[q_idx]

        # For the mean score we also have to produce the reverse score
        if scores in ('mean', 'min', 'max', 'both'):
            reverse = self.single_query_target(t_idx, q_idx, scores='forward')
            if scores == 'mean':
                scr = (scr + reverse) / 2
            elif scores == 'min':
                scr = min(scr, reverse)
            elif scores == 'max':
                scr = max(scr, reverse)
            elif scores == 'both':
                # If both scores are requested
                scr = [scr, reverse]

        return scr

__init__ #

Initialize class.

Source code in navis/nbl/nblast_funcs.py 
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def __init__(self, use_alpha=False, normalized=True, smat='auto',
             limit_dist=None, approx_nn=False, dtype=np.float64,
             progress=True, smat_kwargs=dict()):
    """Initialize class."""
    super().__init__(progress=progress, dtype=dtype)
    self.use_alpha = use_alpha
    self.normalized = normalized
    self.approx_nn = approx_nn
    self.desc = "NBlasting"

    if smat is None:
        self.score_fn = operator.mul
    elif isinstance(smat, pd.DataFrame):
        self.score_fn = Lookup2d.from_dataframe(smat)
    elif isinstance(smat, str) and smat == 'auto':
        self.score_fn = smat_fcwb(self.use_alpha)
    elif isinstance(smat, str) and smat == 'v1':
        self.score_fn = partial(
            _nblast_v1_scoring, sigma_scoring = smat_kwargs.get('sigma_scoring', 10)
        )
    else:
        self.score_fn = smat

    if limit_dist == "auto":
        try:
            if self.score_fn.axes[0].boundaries[-1] != np.inf:
                self.distance_upper_bound = self.score_fn.axes[0].boundaries[-1]
            else:
                # If the right boundary is open (i.e. infinity), we will use
                # the second highest boundary plus a 5% offset
                self.distance_upper_bound = self.score_fn.axes[0].boundaries[-2] * 1.05
        except AttributeError:
            logger.warning("Could not infer distance upper bound from scoring function")
            self.distance_upper_bound = None
    else:
        self.distance_upper_bound = limit_dist

Append dotprops.

Returns the numerical index appended dotprops. If dotprops is a (possibly nested) sequence of dotprops, return a (possibly nested) list of indices.

Note that self_hit is ignored (and hence calculated from scratch) when dotprops is a nested list of dotprops.

Source code in navis/nbl/nblast_funcs.py 
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def append(self, dotprops: Dotprops, self_hit: Optional[float] = None) -> NestedIndices:
    """Append dotprops.

    Returns the numerical index appended dotprops.
    If dotprops is a (possibly nested) sequence of dotprops,
    return a (possibly nested) list of indices.

    Note that `self_hit` is ignored (and hence calculated from scratch)
    when `dotprops` is a nested list of dotprops.
    """
    if isinstance(dotprops, Dotprops):
        return self._append_dotprops(dotprops, self_hit=self_hit)

    try:
        return [self.append(n) for n in dotprops]
    except TypeError:  # i.e. not iterable
        raise ValueError(f"Expected Dotprops or iterable thereof; got {type(dotprops)}")

Non-normalized value for self hit.

Source code in navis/nbl/nblast_funcs.py 
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def calc_self_hit(self, dotprops):
    """Non-normalized value for self hit."""
    if not self.use_alpha:
        return len(dotprops.points) * self.score_fn(0, 1.0)
    else:
        dists = np.repeat(0, len(dotprops.points))
        alpha = dotprops.alpha * dotprops.alpha
        dots = np.repeat(1, len(dotprops.points)) * np.sqrt(alpha)
        return self.score_fn(dists, dots).sum()

Query single target against single target.

Source code in navis/nbl/nblast_funcs.py 
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def single_query_target(self, q_idx: int, t_idx: int, scores='forward'):
    """Query single target against single target."""
    # Take a short-cut if this is a self-self comparison
    if q_idx == t_idx:
        if self.normalized:
            return 1
        return self.self_hits[q_idx]

    # Run nearest-neighbor search for query against target
    data = self.neurons[q_idx].dist_dots(self.neurons[t_idx],
                                         alpha=self.use_alpha,
                                         # eps=0.1 means we accept 10% inaccuracy
                                         eps=.1 if self.approx_nn else 0,
                                         distance_upper_bound=self.distance_upper_bound)
    if self.use_alpha:
        dists, dots, alpha = data
        dots *= np.sqrt(alpha)
    else:
        dists, dots = data

    scr = self.score_fn(dists, dots).sum()

    # Normalize against best hit
    if self.normalized:
        scr /= self.self_hits[q_idx]

    # For the mean score we also have to produce the reverse score
    if scores in ('mean', 'min', 'max', 'both'):
        reverse = self.single_query_target(t_idx, q_idx, scores='forward')
        if scores == 'mean':
            scr = (scr + reverse) / 2
        elif scores == 'min':
            scr = min(scr, reverse)
        elif scores == 'max':
            scr = max(scr, reverse)
        elif scores == 'both':
            # If both scores are requested
            scr = [scr, reverse]

    return scr

Align data types of dotprops.

PARAMETER DESCRIPTION
*x

TYPE: Dotprops | NeuronLists thereof DEFAULT: ()

downcast 
    If True, will downcast all points to the lowest precision
    dtype.

TYPE: bool DEFAULT: False

inplace 
    If True, will modify the original neuron objects. If False, will
    make a copy before changing dtypes.

TYPE: bool DEFAULT: True

RETURNS DESCRIPTION
*x

Input data with aligned dtypes.
Source code in navis/nbl/nblast_funcs.py 
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def align_dtypes(*x, downcast=False, inplace=True):
    """Align data types of dotprops.

    Parameters
    ----------
    *x :        Dotprops | NeuronLists thereof
    downcast :  bool
                If True, will downcast all points to the lowest precision
                dtype.
    inplace :   bool
                If True, will modify the original neuron objects. If False, will
                make a copy before changing dtypes.

    Returns
    -------
    *x
                Input data with aligned dtypes.

    """
    dtypes = get_dtypes(x)

    if len(dtypes) == 1:
        return x

    if not inplace:
        for i in range(x):
            x[i] = x[i].copy()

    if downcast:
        target = lowest_type(*x)
    else:
        target = np.result_type(*dtypes)

    for n in x:
        if isinstance(n, NeuronList):
            for i in range(len(n)):
                n[i].points = n[i].points.astype(target, copy=False)
        elif isinstance(n, Dotprops):
            n.points = n.points.astype(target, copy=False)
        else:
            raise TypeError(f'Unable to process "{type(n)}"')

    return x

Check if neuron data is in microns.

Returns either [True, None (=unclear), False]

Source code in navis/nbl/nblast_funcs.py 
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def check_microns(x):
    """Check if neuron data is in microns.

    Returns either [True, None (=unclear), False]
    """
    microns = [config.ureg.Unit('microns'),
               config.ureg.Unit('um'),
               config.ureg.Unit('micrometer'),
               config.ureg.Unit('dimensionless')]
    if not isinstance(x, NeuronList):
        x = NeuronList(x)

    # For very large NeuronLists converting the unit string to pint units is
    # the time consuming step. Here we will first reduce to unique units:
    unit_str = []
    for n in x:
        if isinstance(n._unit_str, str):
            unit_str.append(n._unit_str)
        else:
            unit_str += list(n._unit_str)
    unit_str = np.unique(unit_str)

    any_not_microns = False
    all_units = True
    for u in unit_str:
        # If not a unit (i.e. `None`)
        if not u:
            all_units = False
            continue

        # Convert to proper unit
        u = config.ureg(u).to_compact().units

        if u not in microns:
            any_not_microns = True

    if any_not_microns:
        return False
    elif all_units:
        return True
    return None

Check if pykdtree is used and if the OMP_NUM_THREADS flag is set.

The issue is that on Linux pykdtree uses threads by default which causes issues when we're also using multiple cores (= multiple layers of concurrency).

Source code in navis/nbl/nblast_funcs.py 
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def check_pykdtree_flag():
    """Check if pykdtree is used and if the OMP_NUM_THREADS flag is set.

    The issue is that on Linux pykdtree uses threads by default which causes
    issues when we're also using multiple cores (= multiple layers of concurrency).
    """
    # This is only relevant for Linux (unless someone compiled pykdtree
    # themselves using a compiler that supports openmp)
    from sys import platform
    if platform not in ("linux", "linux2"):
        return

    # See if pykdtree is present
    try:
        import pykdtree
    except ModuleNotFoundError:
        # If not present, just return
        return

    import os
    if os.environ.get('OMP_NUM_THREADS', None) != "1":
        msg = ('`OMP_NUM_THREADS` environment variable not set to 1. This may '
               'result in multiple layers of concurrency which in turn will '
               'slow down NBLAST when using multiple cores. '
               'See also https://github.com/navis-org/navis/issues/49')
        logger.warning(msg)

Determine the lower of two dtypes.

Source code in navis/nbl/nblast_funcs.py 
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def demote_types(a, b):
    """Determine the lower of two dtypes."""
    if isinstance(a, np.ndarray):
        a = a.dtype
    if isinstance(b, np.ndarray):
        b = b.dtype

    # No change is same
    if a == b:
        return a

    # First, get the "higher" type
    higher = np.promote_types(a, b)
    # Now get the one that's not higher
    if a != higher:
        return a
    return b

Evaluate limit_dist parameter.

Source code in navis/nbl/nblast_funcs.py 
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def eval_limit_dist(x):
    """Evaluate `limit_dist` parameter."""
    if x == 'auto':
        return
    if isinstance(x, type(None)):
        return
    if isinstance(x, numbers.Number):
        return

    raise ValueError(f'`limit_dist` must be None, "auto" or float, got {x}' )

Find partitions such that each batch takes about T seconds.

PARAMETER DESCRIPTION
q 
    Query and targets, respectively.

T 
    Time (in seconds) to aim for.

TYPE: int DEFAULT: 10

n_cores 
    Number of cores that will be used. If provided, will try to
    make sure that (n_rows * n_cols) is a multiple of n_cores by
    increasing the number of rows (thereby decreasing the time
    per batch).

TYPE: int DEFAULT: None

RETURNS DESCRIPTION
(n_rows, n_cols)
Source code in navis/nbl/nblast_funcs.py 
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def find_batch_partition(q, t, T=10, n_cores=None):
    """Find partitions such that each batch takes about `T` seconds.

    Parameters
    ----------
    q,t :       NeuronList of Dotprops
                Query and targets, respectively.
    T :         int
                Time (in seconds) to aim for.
    n_cores :   int, optional
                Number of cores that will be used. If provided, will try to
                make sure that (n_rows * n_cols) is a multiple of n_cores by
                increasing the number of rows (thereby decreasing the time
                per batch).

    Returns
    -------
    n_rows, n_cols

    """
    # Test a single query
    time_per_query = test_single_query_time(q, t)

    # Number of queries per job such that each job runs in `T` second
    queries_per_batch = T / time_per_query

    # Number of neurons per batch
    neurons_per_batch  = max(1, int(np.sqrt(queries_per_batch)))

    n_rows = max(1, len(q) // neurons_per_batch)
    n_cols = max(1, len(t) // neurons_per_batch)

    if n_cores and ((n_rows * n_cols) > n_cores):
        while (n_rows * n_cols) % n_cores:
            n_rows += 1

    return n_rows, n_cols

Force data into Dotprops.

Source code in navis/nbl/nblast_funcs.py 
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def force_dotprops(x, k, resample, progress=False):
    """Force data into Dotprops."""
    if isinstance(x, (NeuronList, list)):
        dp = [force_dotprops(n, k, resample) for n in config.tqdm(x,
                                                                  desc='Dotprops',
                                                                  disable=not progress,
                                                                  leave=False)]
        return NeuronList(dp)

    # Try converting non-Dotprops
    if not isinstance(x, Dotprops):
        return make_dotprops(x, k=k, resample=resample)

    # Return Dotprops
    return x

Collect data types of dotprops points.

Source code in navis/nbl/nblast_funcs.py 
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def get_dtypes(*x):
    """Collect data types of dotprops points."""
    dtypes = set()
    for n in x:
        if isinstance(n, NeuronList):
            dtypes = dtypes | get_dtypes(n)
        elif isinstance(n, Dotprops):
            dtypes.add(n.points.dtype)
        else:
            raise TypeError(f'Unable to process "{type(n)}"')
    return dtypes

Find the lowest data type.

Source code in navis/nbl/nblast_funcs.py 
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def lowest_type(*x):
    """Find the lowest data type."""
    dtypes = get_dtypes(x)

    if len(dtypes) == 1:
        return dtypes[0]

    lowest = dtypes[0]
    for dt in dtypes[1:]:
        lowest = demote_types(lowest, dt)

    return lowest

Convert similarity scores to distances.

PARAMETER DESCRIPTION
x 
Similarity score matrix to invert.

TYPE: (M, M) np.ndarray | pandas.DataFrame

RETURNS DESCRIPTION
distances
Source code in navis/nbl/nblast_funcs.py 
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def sim_to_dist(x):
    """Convert similarity scores to distances.

    Parameters
    ----------
    x :     (M, M) np.ndarray | pandas.DataFrame
            Similarity score matrix to invert.

    Returns
    -------
    distances

    """
    if not isinstance(x, (np.ndarray, pd.DataFrame)):
        raise TypeError(f'Expected numpy array or pandas DataFrame, got "{type(x)}"')

    if isinstance(x, pd.DataFrame):
        mx = x.values.max()
    else:
        mx = x.max()

    return (x - mx) * -1
Source code in navis/nbl/smat.py 
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def smat_fcwb(alpha=False):
    # deepcopied so that mutations do not propagate to cache
    return deepcopy(_smat_fcwb(alpha))

Test average time of a single NBLAST query.

Source code in navis/nbl/nblast_funcs.py 
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def test_single_query_time(q, t, it=100):
    """Test average time of a single NBLAST query."""
    # Get a median-sized query and target
    q_ix = np.argsort(q.n_points)[len(q)//2]
    t_ix = np.argsort(t.n_points)[len(t)//2]

    # Run a quick single query benchmark
    timings = []
    for i in range(it):  # Run N tests
        s = time.time()
        _ = t[t_ix].dist_dots(q[q_ix])  # Dist dot (ignore scoring / normalizing)
        timings.append(time.time() - s)
    return np.mean(timings)  # seconds per medium sized query