Module pept.base.point_data

Classes

class PointData (point_data, sample_size=0, overlap=0, verbose=False)

A class for generic PEPT data iteration, manipulation and visualisation.

This class is used to encapsulate points. Unlike LineData, it does not have any restriction on the maximum number of columns it can store. It can yield samples of the point_data of an adaptive sample_size and overlap, without requiring additional storage.

Parameters

point_data : (N, M) numpy.ndarray

An (N, M >= 4) numpy array that stores points (or any generic 2D set of data). It expects that the first column is time, followed by cartesian (3D) coordinates of points in mm, followed by any extra information the user needs. A row is then [time, x, y, z, etc].

sample_size : int, optional

An int`` that defines the number of points that should be returned when iterating overpoint_data. Asample_size` of 0 yields all the data as one single sample. (Default is 200)

overlap : int, optional

An int that defines the overlap between two consecutive samples that are returned when iterating over point_data. An overlap of 0 means consecutive samples, while an overlap of (sample_size - 1) means incrementing the samples by one. A negative overlap means skipping values between samples. An error is raised if overlap is larger than or equal to sample_size. (Default is 0)

verbose : bool, optional

An option that enables printing the time taken for the initialisation of an instance of the class. Useful when reading large files (10gb files for PEPT data is not unheard of). (Default is True)

Attributes

point_data : (N, M) numpy.ndarray

An (N, M >= 4) numpy array that stores the points as time, followed by cartesian (3D) coordinates of the point in mm, followed by any extra information. Each row is then [time, x, y, z, etc].

sample_size : int

An int that defines the number of lines that should be returned when iterating over point_data. (Default is 200)

overlap : int

An int that defines the overlap between two consecutive samples that are returned when iterating over point_data. An overlap of 0 means consecutive samples, while an overlap of (sample_size - 1) means incrementing the samples by one. A negative overlap means skipping values between samples. It is required to be smaller than sample_size. (Default is 0)

number_of_points : int

An int that corresponds to len(point_data), or the number of points stored by point_data.

number_of_samples : int

An int that corresponds to the number of samples that can be accessed from the class, taking the overlap into consideration.

Raises

ValueError

If overlap >= sample_size. Overlap is required to be smaller than sample_size, unless sample_size is 0. Note that it can also be negative.

ValueError

If line_data does not have (N, M) shape, where M >= 4.

Notes

The class saves point_data as a contiguous numpy array for efficient access in C extensions.

Source code

class PointData:
    '''A class for generic PEPT data iteration, manipulation and visualisation.

    This class is used to encapsulate points. Unlike `LineData`, it does not have
    any restriction on the maximum number of columns it can store. It can yield
    samples of the `point_data` of an adaptive `sample_size` and `overlap`,
    without requiring additional storage.

    Parameters
    ----------
    point_data : (N, M) numpy.ndarray
        An (N, M >= 4) numpy array that stores points (or any generic 2D set of
        data). It expects that the first column is time, followed by cartesian
        (3D) coordinates of points **in mm**, followed by any extra information
        the user needs. A row is then [time, x, y, z, etc].
    sample_size : int, optional
        An `int`` that defines the number of points that should be
        returned when iterating over `point_data`. A `sample_size` of 0
        yields all the data as one single sample. (Default is 200)
    overlap : int, optional
        An `int` that defines the overlap between two consecutive
        samples that are returned when iterating over `point_data`.
        An overlap of 0 means consecutive samples, while an overlap
        of (`sample_size` - 1) means incrementing the samples by one.
        A negative overlap means skipping values between samples. An
        error is raised if `overlap` is larger than or equal to
        `sample_size`. (Default is 0)
    verbose : bool, optional
        An option that enables printing the time taken for the
        initialisation of an instance of the class. Useful when
        reading large files (10gb files for PEPT data is not unheard
        of). (Default is True)

    Attributes
    ----------
    point_data : (N, M) numpy.ndarray
        An (N, M >= 4) numpy array that stores the points as time, followed by
        cartesian (3D) coordinates of the point **in mm**, followed by any extra
        information. Each row is then `[time, x, y, z, etc]`.
    sample_size : int
        An `int` that defines the number of lines that should be
        returned when iterating over `point_data`. (Default is 200)
    overlap : int
        An `int` that defines the overlap between two consecutive
        samples that are returned when iterating over `point_data`.
        An overlap of 0 means consecutive samples, while an overlap
        of (`sample_size` - 1) means incrementing the samples by one.
        A negative overlap means skipping values between samples. It
        is required to be smaller than `sample_size`. (Default is 0)
    number_of_points : int
        An `int` that corresponds to len(`point_data`), or the number of
        points stored by `point_data`.
    number_of_samples : int
        An `int` that corresponds to the number of samples that can be
        accessed from the class, taking the `overlap` into consideration.

    Raises
    ------
    ValueError
        If `overlap` >= `sample_size`. Overlap is required to be smaller
        than `sample_size`, unless `sample_size` is 0. Note that it can
        also be negative.
    ValueError
        If `line_data` does not have (N, M) shape, where M >= 4.

    Notes
    -----
    The class saves `point_data` as a **contiguous** numpy array for
    efficient access in C extensions.

    '''


    def __init__(
        self,
        point_data,
        sample_size = 0,
        overlap = 0,
        verbose = False
    ):

        if verbose:
            start = time.time()

        if sample_size < 0:
            raise ValueError('\n[ERROR]: sample_size = {} must be positive (>= 0)'.format(sample_size))
        if sample_size != 0 and overlap >= sample_size:
            raise ValueError('\n[ERROR]: overlap = {} must be smaller than sample_size = {}\n'.format(overlap, sample_size))

        self._index = 0
        self._sample_size = sample_size
        self._overlap = overlap

        self._point_data = np.asarray(point_data, order = 'C', dtype = float)

        if self._point_data.ndim != 2 or self._point_data.shape[1] < 4:
            raise ValueError('\n[ERROR]: point_data should have two dimensions (M, N), where N >= 4. Received {}\n'.format(self._point_data.shape))

        self._number_of_points = len(self._point_data)

        if verbose:
            end = time.time()
            print("Initialising the PEPT data took {} seconds\n".format(end - start))


    @property
    def point_data(self):
        '''Get the points stored in the class.

        Returns
        -------
        (M, N) numpy.ndarray
            A memory view of the points stored in `point_data`.

        '''

        return self._point_data


    @property
    def sample_size(self):
        '''Get the number of points in one sample returned by the class.

        Returns
        -------
        int
            The sample size (number of lines) in one sample returned by
            the class.

        '''

        return self._sample_size


    @sample_size.setter
    def sample_size(self, new_sample_size):
        '''Change `sample_size` without instantiating a new object

        It also resets the inner index of the class.

        Parameters
        ----------
        new_sample_size : int
            The new sample size. It has to be larger than `overlap`,
            unless it is 0 (in which case all `point_data` will be returned
            as one sample).

        Raises
        ------
        ValueError
            If `overlap` >= `new_sample_size`. Overlap has to be
            smaller than `sample_size`, unless `sample_size` is 0.
            Note that it can also be negative.

        '''

        if new_sample_size < 0:
            raise ValueError('\n[ERROR]: sample_size = {} must be positive (>= 0)'.format(new_sample_size))
        if new_sample_size != 0 and self._overlap >= new_sample_size:
            raise ValueError('\n[ERROR]: overlap = {} must be smaller than new_sample_size = {}\n'.format(self._overlap, new_sample_size))

        self._index = 0
        self._sample_size = new_sample_size


    @property
    def overlap(self):
        '''Get the overlap between every two samples returned by the class.

        Returns
        -------
        int
            The overlap (number of points) between every two samples  returned by
            the class.

        '''

        return self._overlap


    @overlap.setter
    def overlap(self, new_overlap):
        '''Change `overlap` without instantiating a new object

        It also resets the inner index of the class.

        Parameters
        ----------
        new_overlap : int
            The new overlap. It has to be smaller than `sample_size`, unless
            `sample_size` is 0 (in which case all `point_data` will be returned
            as one sample and so overlap does not play any role).

        Raises
        ------
        ValueError
            If `new_overlap` >= `sample_size`. `new_overlap` has to be
            smaller than `sample_size`, unless `sample_size` is 0.
            Note that it can also be negative.

        '''

        if self._sample_size != 0 and new_overlap >= self._sample_size:
            raise ValueError('\n[ERROR]: new_overlap = {} must be smaller than sample_size = {}\n'.format(new_overlap, self._sample_size))

        self._index = 0
        self._overlap = new_overlap


    @property
    def number_of_samples(self):
        '''Get number of samples, considering overlap.

        If `sample_size == 0`, all data is returned as a single sample,
        and so `number_of_samples` will be 1. Otherwise, it checks the
        number of samples every time it is called, taking `overlap` into
        consideration.

        Returns
        -------
        int
            The number of samples, taking `overlap` into consideration.

        '''
        # If self.sample_size == 0, all data is returned as a single sample
        if self._sample_size == 0:
            return 1

        # If self.sample_size != 0, check there is at least one sample
        if self._number_of_points >= self._sample_size:
            return (self._number_of_points - self._sample_size) // (self.sample_size - self.overlap) + 1
        else:
            return 0


    @property
    def number_of_points(self):
        '''Get the number of points stored in the class.

        Returns
        -------
        int
            The number of points stored in `point_data`.

        '''
        return self._number_of_points


    def sample_n(self, n):
        '''Get sample number n (indexed from 1, i.e. `n > 0`)

        Returns the lines from `point_data` included in sample number
        `n`. Samples are numbered starting from 1.

        Parameters
        ----------
        n : int
            The number of the sample required. Note that `1 <= n <=
            number_of_samples`.

        Returns
        -------
        (M, N) numpy.ndarray
            A shallow copy of the points from `point_data` included in
            sample number n.

        Raises
        ------
        IndexError
            If `sample_size == 0`, all data is returned as one single
            sample. Raised if `n` is not 1.
        IndexError
            If `n > number_of_samples` or `n <= 0`.

        '''
        if self._sample_size == 0:
            if n == 1:
                return self._point_data
            else:
                raise IndexError("\n\n[ERROR]: Trying to access a non-existent sample (samples indexed from 1): asked for sample number {}, when there is only 1 sample (sample_size == 0)\n".format(n))
        elif (n > self.number_of_samples) or n <= 0:
            raise IndexError("\n\n[ERROR]: Trying to access a non-existent sample (samples are indexed from 1): asked for sample number {}, when there are {} samples\n".format(n, self.number_of_samples))

        start_index = (n - 1) * (self._sample_size - self._overlap)
        return self._point_data[start_index:(start_index + self._sample_size)]


    def to_csv(self, filepath, delimiter = '  ', newline = '\n'):
        '''Write `point_data` to a CSV file

        Write all points (and any extra data) stored in the class to a CSV file.

        Parameters
        ----------
            filepath : filename or file handle
                If filepath is a path (rather than file handle), it is relative
                to where python is called.
            delimiter : str, optional
                The delimiter between values. The default is two spaces '  ',
                such that numbers in the format '123,456.78' are well-understood.
            newline : str, optional
                The sequence of characters at the end of every line. The default
                is a new line '\n'

        '''
        np.savetxt(filepath, self._point_data, delimiter = delimiter, newline = newline)


    def plot_all_points(self, ax = None):
        '''Plot all points using matplotlib

        Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points on it.

        Parameters
        ----------
        ax : mpl_toolkits.mplot3D.Axes3D object
            The 3D matplotlib-based axis for plotting.

        Returns
        -------
        fig, ax : matplotlib figure and axes objects

        Note
        ----
        Plotting all points in the case of large LoR arrays is *very*
        computationally intensive. For large arrays (> 10000), plotting
        individual samples using `plot_points_sample_n` is recommended.

        '''

        if ax == None:
            fig = plt.figure()
            ax  = fig.add_subplot(111, projection='3d')
        else:
            fig = plt.gcf()

        # Scatter x, y, z, [color]

        x = self._point_data[:, 1],
        y = self._point_data[:, 2],
        z = self._point_data[:, 3],

        color = self._point_data[:, -1],

        cmap = plt.cm.magma
        color_array = cmap(colour_data)

        ax.scatter(x,y,z,c=color_array[0])

        return fig, ax


    def plot_all_points_alt_axes(self, ax = None ):
        '''Plot all points using matplotlib on PEPT-style axes

        Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points on
        the PEPT-style convention: **x** is *parallel and horizontal* to the
        screens, **y** is *parallel and vertical* to the screens, **z** is
        *perpendicular* to the screens. The mapping relative to the
        Cartesian coordinates would then be: (x, y, z) -> (z, x, y)

        Parameters
        ----------
        ax : mpl_toolkits.mplot3D.Axes3D object
            The 3D matplotlib-based axis for plotting.

        Returns
        -------
        fig, ax : matplotlib figure and axes objects

        Note
        ----
        Plotting all points in the case of large LoR arrays is *very*
        computationally intensive. For large arrays (> 10000), plotting
        individual samples using `plot_lines_sample_n_alt_axes` is recommended.

        '''

        if ax == None:
            fig = plt.figure()
            ax  = fig.add_subplot(111, projection='3d')
        else:
            fig = plt.gcf()

        # Scatter x, y, z, [color]

        x = self._point_data[:, 1]
        y = self._point_data[:, 2]
        z = self._point_data[:, 3]

        color = self._point_data[:, -1]

        cmap = plt.cm.magma
        color_array = cmap(color)

        ax.scatter(z,x,y,c=color_array[0])

        return fig, ax


    def plot_points_sample_n(self, n, ax=None):
        '''Plot points from sample `n` using matplotlib

        Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points
        from sample number `n`.

        Parameters
        ----------
        ax : mpl_toolkits.mplot3D.Axes3D object
            The 3D matplotlib-based axis for plotting.
        n : int
            The number of the sample to be plotted.

        Returns
        -------

        fig, ax : matplotlib figure and axes objects

        '''

        if ax == None:
            fig = plt.figure()
            ax  = fig.add_subplot(111, projection='3d')
        else:
            fig = plt.gcf()

        # Scatter x, y, z, [color]

        sample = self.sample_n(n)

        x = sample[:, 1]
        y = sample[:, 2]
        z = sample[:, 3]

        color = sample[:, -1]

        cmap = plt.cm.magma
        color_array = cmap(color)

        ax.scatter(z,x,y,c=color_array[0])

        return fig, ax


    def plot_points_sample_n_alt_axes(self, n, ax=None):
        '''Plot points from sample `n` using matplotlib on PEPT-style axes

        Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points from
        sample number sampleN on the PEPT-style coordinates convention:
        **x** is *parallel and horizontal* to the screens, **y** is
        *parallel and vertical* to the screens, **z** is *perpendicular*
        to the screens. The mapping relative to the Cartesian coordinates
        would then be: (x, y, z) -> (z, x, y)

        Parameters
        ----------
        ax : mpl_toolkits.mplot3D.Axes3D object
            The 3D matplotlib-based axis for plotting.
        n : int
            The number of the sample to be plotted.

        Returns
        -------

        fig, ax : matplotlib figure and axes objects
        '''

        if ax == None:
            fig = plt.figure()
            ax  = fig.add_subplot(111, projection='3d')
        else:
            fig = plt.gcf()

        # Scatter x, y, z, [color]

        sample = self.sample_n(n)

        x = sample[:, 1]
        y = sample[:, 2]
        z = sample[:, 3]

        color = sample[:, -1]

        cmap = plt.cm.magma
        color_array = cmap(color)

        ax.scatter(z,x,y,c=color_array[0])

        return fig, ax


    def all_points_trace(self, size = 2, color = None):
        '''Get a Plotly trace of all points.

        Creates a `plotly.graph_objects.Scatter3d` object. Can
        then be passed to the `plotly.graph_objects.figure.add_trace`
        function or a `PlotlyGrapher` instance using the `add_trace` method.

        Returns
        -------
        plotly.graph_objects.Scatter3d
            A `plotly.graph_objects.Scatter3d` trace of all points.

        Note
        ----
        Plotting all points in the case of large LoR arrays is *very*
        computationally intensive. For large arrays (> 10000), plotting
        individual samples using `points_sample_n_traces` is recommended.

        '''

        trace = go.Scatter3d(
            x = self._point_data[:, 1],
            y = self._point_data[:, 2],
            z = self._point_data[:, 3],
            mode = 'markers',
            marker = dict(
                size = size,
                color = color,
                opacity = 0.8
            )
        )

        return trace


    def all_points_trace_colorbar(self, size = 2, colorbar_title = None):
        '''Get a Plotly trace of all points, colour-coding the last column of `point_data`.

        Creates a `plotly.graph_objects.Scatter3d` object. Can
        then be passed to the `plotly.graph_objects.figure.add_trace`
        function or a `PlotlyGrapher` instance using the `add_trace` method.

        Returns
        -------
        plotly.graph_objects.Scatter3d
            A `plotly.graph_objects.Scatter3d` trace of all points.

        Note
        ----
        Plotting all points in the case of large LoR arrays is *very*
        computationally intensive. For large arrays (> 10000), plotting
        individual samples using `points_sample_n_traces` is recommended.

        '''

        if colorbar_title != None:
            colorbar = dict(title = colorbar_title)
        else:
            colorbar = dict()

        trace = go.Scatter3d(
            x = self._point_data[:, 1],
            y = self._point_data[:, 2],
            z = self._point_data[:, 3],
            mode = 'markers',
            marker = dict(
                size = size,
                color = self._point_data[:, -1],
                colorscale = 'Magma',
                colorbar = colorbar,
                opacity = 0.8
            )
        )

        return trace


    def points_sample_n_trace(self, n, size = 2, color = None):
        '''Get a Plotly trace for all points in sample `n`.

        Returns a `plotly.graph_objects.Scatter3d` trace containing all points
        included in sample number `n`.
        Can then be passed to the `plotly.graph_objects.figure.add_trace`
        function or a `PlotlyGrapher` instance using the `add_trace` method.

        Parameters
        ----------
        n : int
            The number of the sample to be plotted.

        Returns
        -------
        plotly.graph_object.Scatter3d
            A `plotly.graph_objects.Scatter3d` trace of all points in sample `n`.

        '''

        sample = self.sample_n(n)
        trace = go.Scatter3d(
            x = sample[:, 1],
            y = sample[:, 2],
            z = sample[:, 3],
            mode = 'markers',
            marker = dict(
                size = size,
                color = color,
                opacity = 0.8
            )
        )

        return trace


    def points_sample_n_trace_colorbar(self, n, size = 2, colorbar_title = None):
        '''Get a Plotly trace for all points in sample `n`, colour-coding the last column.

        Returns a `plotly.graph_objects.Scatter3d` trace containing all points
        included in sample number `n`.
        Can then be passed to the `plotly.graph_objects.figure.add_trace`
        function or a `PlotlyGrapher` instance using the `add_trace` method.

        Parameters
        ----------
        n : int
            The number of the sample to be plotted.

        Returns
        -------
        plotly.graph_object.Scatter3d
            A `plotly.graph_objects.Scatter3d` trace of all points in sample `n`.

        '''

        if colorbar_title != None:
            colorbar = dict(title = colorbar_title)
        else:
            colorbar = dict()

        sample = self.sample_n(n)
        trace = go.Scatter3d(
            x = sample[:, 1],
            y = sample[:, 2],
            z = sample[:, 3],
            mode = 'markers',
            marker = dict(
                size = size,
                color = sample[:, -1],
                colorscale = 'Magma',
                colorbar = colorbar,
                opacity = 0.8
            )
        )

        return trace


    def points_trace(
        self,
        sample_indices = 0,
        size = 2,
        color = None,
        opacity = 0.8,
        colorbar = False,
        colorbar_col = -1,
        colorbar_title = None
    ):
        '''Get a Plotly trace for all points in selected samples, with possible color-coding.

        Returns a `plotly.graph_objects.Scatter3d` trace containing all points
        included in in the samples selected by `sample_indices`. `sample_indices`
        can be a single sample index (e.g. 0) or an iterable of indices (e.g.
        [1,5,6]).
        Can then be passed to the `plotly.graph_objects.figure.add_trace`
        function or a `PlotlyGrapher` instance using the `add_trace` method.

        Parameters
        ----------
        sample_indices : int or iterable
            The index or indices of the samples of LoRs. The default is 0 (the first sample).
        size : float
            The marker size of the points. The default is 2.
        color : str or list-like
            Can be a single color (e.g. "black", "rgb(122, 15, 241)") or a colorbar list.
            Is ignored if `colorbar` is set to True. For more information, check the Plotly
            documentation. The default is None.
        opacity : float
            The opacity of the lines, where 0 is transparent and 1 is fully
            opaque. The default is 0.8.
        colorbar : bool
            If set to True, will color-code the data in the sample column `colorbar_col`.
            Overrides `color` if set to True. The default is False.
        colorbar_col : int
            The column in the data samples that will be used to color the points. Only has
            an effect if `colorbar` is set to True. The default is -1 (the last column).
        colorbar_title : str
            If set, the colorbar will have this title above. The default is None.

        Returns
        -------
        plotly.graph_objs.Scatter3d
            A Plotly trace of the points.

        '''

        # Check if sample_indices is an iterable collection (list-like)
        # otherwise just "iterate" over the single number
        if not hasattr(sample_indices, "__iter__"):
            sample_indices = [sample_indices]

        coords_x = []
        coords_y = []
        coords_z = []

        marker = dict(
            size = size,
            color = color,
            opacity = opacity
        )

        if colorbar:
            marker['color'] = []
            marker.update(colorscale = "Magma")

            if colorbar_title is not None:
                marker.update(colorbar = dict(title = colorbar_title))

        # For each selected sample include all the needed coordinates
        for n in sample_indices:
            sample = self[n]

            coords_x.extend(sample[:, 1])
            coords_y.extend(sample[:, 2])
            coords_z.extend(sample[:, 3])

            if colorbar == True:
                marker['color'].extend(sample[:, colorbar_col])

        trace = go.Scatter3d(
            x = coords_x,
            y = coords_y,
            z = coords_z,
            mode = "markers",
            marker = marker
        )

        return trace


    def __len__(self):
        # Defined so that len(class_instance) returns the number of samples.

        return self.number_of_samples


    def __str__(self):
        # Shown when calling print(class)
        docstr = ""

        docstr += "number_of_points =  {}\n\n".format(self.number_of_points)
        docstr += "sample_size =       {}\n".format(self._sample_size)
        docstr += "overlap =           {}\n".format(self._overlap)
        docstr += "number_of_samples = {}\n\n".format(self.number_of_samples)
        docstr += "point_data = \n"
        docstr += self._point_data.__str__()

        return docstr


    def __repr__(self):
        # Shown when writing the class on a REPR

        docstr = "Class instance that inherits from `pept.PointData`.\n\n" + self.__str__() + "\n\n"
        docstr += "Particular cases:\n"
        docstr += " > If sample_size == 0, all point_data is returned as one single sample.\n"
        docstr += " > If overlap >= sample_size, an error is raised.\n"
        docstr += " > If overlap < 0, points are skipped between samples.\n"

        return docstr


    def __getitem__(self, key):
        # Defined so that samples can be accessed as class_instance[0]

        if self.number_of_samples == 0:
            raise IndexError("Tried to access sample {} (indexed from 0), when there are {} samples".format(key, self.number_of_samples))

        if key >= self.number_of_samples:
            raise IndexError("Tried to access sample {} (indexed from 0), when there are {} samples".format(key, self.number_of_samples))


        while key < 0:
            key += self.number_of_samples

        return self.sample_n(key + 1)


    def __iter__(self):
        # Defined so the class can be iterated as `for sample in class_instance: ...`
        return self


    def __next__(self):
        # sample_size = 0 => return all data
        if self._sample_size == 0:
            self._sample_size = -1
            return self._point_data
        # Use -1 as a flag
        if self._sample_size == -1:
            self._sample_size = 0
            raise StopIteration

        # sample_size > 0 => return slices
        if self._index != 0:
            self._index = self._index + self._sample_size - self.overlap
        else:
            self._index = self._index + self.sample_size


        if self._index > self.number_of_points:
            self._index = 0
            raise StopIteration

        return self._point_data[(self._index - self._sample_size):self._index]

Subclasses

• Cutpoints

Instance variables

var number_of_points

Get the number of points stored in the class.

Returns

int

The number of points stored in point_data.

Source code

@property
def number_of_points(self):
    '''Get the number of points stored in the class.

    Returns
    -------
    int
        The number of points stored in `point_data`.

    '''
    return self._number_of_points

var number_of_samples

Get number of samples, considering overlap.

If sample_size == 0, all data is returned as a single sample, and so number_of_samples will be 1. Otherwise, it checks the number of samples every time it is called, taking overlap into consideration.

Returns

int

The number of samples, taking overlap into consideration.

Source code

@property
def number_of_samples(self):
    '''Get number of samples, considering overlap.

    If `sample_size == 0`, all data is returned as a single sample,
    and so `number_of_samples` will be 1. Otherwise, it checks the
    number of samples every time it is called, taking `overlap` into
    consideration.

    Returns
    -------
    int
        The number of samples, taking `overlap` into consideration.

    '''
    # If self.sample_size == 0, all data is returned as a single sample
    if self._sample_size == 0:
        return 1

    # If self.sample_size != 0, check there is at least one sample
    if self._number_of_points >= self._sample_size:
        return (self._number_of_points - self._sample_size) // (self.sample_size - self.overlap) + 1
    else:
        return 0

var overlap

Get the overlap between every two samples returned by the class.

Returns

int

The overlap (number of points) between every two samples returned by the class.

Source code

@property
def overlap(self):
    '''Get the overlap between every two samples returned by the class.

    Returns
    -------
    int
        The overlap (number of points) between every two samples  returned by
        the class.

    '''

    return self._overlap

var point_data

Get the points stored in the class.

Returns

(M, N) numpy.ndarray A memory view of the points stored in point_data.

Source code

@property
def point_data(self):
    '''Get the points stored in the class.

    Returns
    -------
    (M, N) numpy.ndarray
        A memory view of the points stored in `point_data`.

    '''

    return self._point_data

var sample_size

Get the number of points in one sample returned by the class.

Returns

int

The sample size (number of lines) in one sample returned by the class.

Source code

@property
def sample_size(self):
    '''Get the number of points in one sample returned by the class.

    Returns
    -------
    int
        The sample size (number of lines) in one sample returned by
        the class.

    '''

    return self._sample_size

Methods

def all_points_trace(self, size=2, color=None)

Get a Plotly trace of all points.

Creates a plotly.graph_objects.Scatter3d object. Can then be passed to the plotly.graph_objects.figure.add_trace function or a PlotlyGrapher instance using the add_trace method.

Returns

plotly.graph_objects.Scatter3d

A plotly.graph_objects.Scatter3d trace of all points.

Note

Plotting all points in the case of large LoR arrays is very computationally intensive. For large arrays (> 10000), plotting individual samples using points_sample_n_traces is recommended.

Source code

def all_points_trace(self, size = 2, color = None):
    '''Get a Plotly trace of all points.

    Creates a `plotly.graph_objects.Scatter3d` object. Can
    then be passed to the `plotly.graph_objects.figure.add_trace`
    function or a `PlotlyGrapher` instance using the `add_trace` method.

    Returns
    -------
    plotly.graph_objects.Scatter3d
        A `plotly.graph_objects.Scatter3d` trace of all points.

    Note
    ----
    Plotting all points in the case of large LoR arrays is *very*
    computationally intensive. For large arrays (> 10000), plotting
    individual samples using `points_sample_n_traces` is recommended.

    '''

    trace = go.Scatter3d(
        x = self._point_data[:, 1],
        y = self._point_data[:, 2],
        z = self._point_data[:, 3],
        mode = 'markers',
        marker = dict(
            size = size,
            color = color,
            opacity = 0.8
        )
    )

    return trace

def all_points_trace_colorbar(self, size=2, colorbar_title=None)

Get a Plotly trace of all points, colour-coding the last column of point_data.

Creates a plotly.graph_objects.Scatter3d object. Can then be passed to the plotly.graph_objects.figure.add_trace function or a PlotlyGrapher instance using the add_trace method.

Returns

plotly.graph_objects.Scatter3d

A plotly.graph_objects.Scatter3d trace of all points.

Note

Plotting all points in the case of large LoR arrays is very computationally intensive. For large arrays (> 10000), plotting individual samples using points_sample_n_traces is recommended.

Source code

def all_points_trace_colorbar(self, size = 2, colorbar_title = None):
    '''Get a Plotly trace of all points, colour-coding the last column of `point_data`.

    Creates a `plotly.graph_objects.Scatter3d` object. Can
    then be passed to the `plotly.graph_objects.figure.add_trace`
    function or a `PlotlyGrapher` instance using the `add_trace` method.

    Returns
    -------
    plotly.graph_objects.Scatter3d
        A `plotly.graph_objects.Scatter3d` trace of all points.

    Note
    ----
    Plotting all points in the case of large LoR arrays is *very*
    computationally intensive. For large arrays (> 10000), plotting
    individual samples using `points_sample_n_traces` is recommended.

    '''

    if colorbar_title != None:
        colorbar = dict(title = colorbar_title)
    else:
        colorbar = dict()

    trace = go.Scatter3d(
        x = self._point_data[:, 1],
        y = self._point_data[:, 2],
        z = self._point_data[:, 3],
        mode = 'markers',
        marker = dict(
            size = size,
            color = self._point_data[:, -1],
            colorscale = 'Magma',
            colorbar = colorbar,
            opacity = 0.8
        )
    )

    return trace

def plot_all_points(self, ax=None)

Plot all points using matplotlib

Given a mpl_toolkits.mplot3d.Axes3D axis, plots all points on it.

Parameters

ax : mpl_toolkits.mplot3D.Axes3D object

The 3D matplotlib-based axis for plotting.

Returns

fig, ax : matplotlib figure and axes objects

 

Note

Plotting all points in the case of large LoR arrays is very computationally intensive. For large arrays (> 10000), plotting individual samples using plot_points_sample_n is recommended.

Source code

def plot_all_points(self, ax = None):
    '''Plot all points using matplotlib

    Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points on it.

    Parameters
    ----------
    ax : mpl_toolkits.mplot3D.Axes3D object
        The 3D matplotlib-based axis for plotting.

    Returns
    -------
    fig, ax : matplotlib figure and axes objects

    Note
    ----
    Plotting all points in the case of large LoR arrays is *very*
    computationally intensive. For large arrays (> 10000), plotting
    individual samples using `plot_points_sample_n` is recommended.

    '''

    if ax == None:
        fig = plt.figure()
        ax  = fig.add_subplot(111, projection='3d')
    else:
        fig = plt.gcf()

    # Scatter x, y, z, [color]

    x = self._point_data[:, 1],
    y = self._point_data[:, 2],
    z = self._point_data[:, 3],

    color = self._point_data[:, -1],

    cmap = plt.cm.magma
    color_array = cmap(colour_data)

    ax.scatter(x,y,z,c=color_array[0])

    return fig, ax

def plot_all_points_alt_axes(self, ax=None)

Plot all points using matplotlib on PEPT-style axes

Given a mpl_toolkits.mplot3d.Axes3D axis, plots all points on the PEPT-style convention: x is parallel and horizontal to the screens, y is parallel and vertical to the screens, z is perpendicular to the screens. The mapping relative to the Cartesian coordinates would then be: (x, y, z) -> (z, x, y)

Parameters

ax : mpl_toolkits.mplot3D.Axes3D object

The 3D matplotlib-based axis for plotting.

Returns

fig, ax : matplotlib figure and axes objects

 

Note

Plotting all points in the case of large LoR arrays is very computationally intensive. For large arrays (> 10000), plotting individual samples using plot_lines_sample_n_alt_axes is recommended.

Source code

def plot_all_points_alt_axes(self, ax = None ):
    '''Plot all points using matplotlib on PEPT-style axes

    Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points on
    the PEPT-style convention: **x** is *parallel and horizontal* to the
    screens, **y** is *parallel and vertical* to the screens, **z** is
    *perpendicular* to the screens. The mapping relative to the
    Cartesian coordinates would then be: (x, y, z) -> (z, x, y)

    Parameters
    ----------
    ax : mpl_toolkits.mplot3D.Axes3D object
        The 3D matplotlib-based axis for plotting.

    Returns
    -------
    fig, ax : matplotlib figure and axes objects

    Note
    ----
    Plotting all points in the case of large LoR arrays is *very*
    computationally intensive. For large arrays (> 10000), plotting
    individual samples using `plot_lines_sample_n_alt_axes` is recommended.

    '''

    if ax == None:
        fig = plt.figure()
        ax  = fig.add_subplot(111, projection='3d')
    else:
        fig = plt.gcf()

    # Scatter x, y, z, [color]

    x = self._point_data[:, 1]
    y = self._point_data[:, 2]
    z = self._point_data[:, 3]

    color = self._point_data[:, -1]

    cmap = plt.cm.magma
    color_array = cmap(color)

    ax.scatter(z,x,y,c=color_array[0])

    return fig, ax

def plot_points_sample_n(self, n, ax=None)

Plot points from sample n using matplotlib

Given a mpl_toolkits.mplot3d.Axes3D axis, plots all points from sample number n.

Parameters

ax : mpl_toolkits.mplot3D.Axes3D object

The 3D matplotlib-based axis for plotting.

n : int

The number of the sample to be plotted.

Returns

fig, ax : matplotlib figure and axes objects

 

Source code

def plot_points_sample_n(self, n, ax=None):
    '''Plot points from sample `n` using matplotlib

    Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points
    from sample number `n`.

    Parameters
    ----------
    ax : mpl_toolkits.mplot3D.Axes3D object
        The 3D matplotlib-based axis for plotting.
    n : int
        The number of the sample to be plotted.

    Returns
    -------

    fig, ax : matplotlib figure and axes objects

    '''

    if ax == None:
        fig = plt.figure()
        ax  = fig.add_subplot(111, projection='3d')
    else:
        fig = plt.gcf()

    # Scatter x, y, z, [color]

    sample = self.sample_n(n)

    x = sample[:, 1]
    y = sample[:, 2]
    z = sample[:, 3]

    color = sample[:, -1]

    cmap = plt.cm.magma
    color_array = cmap(color)

    ax.scatter(z,x,y,c=color_array[0])

    return fig, ax

def plot_points_sample_n_alt_axes(self, n, ax=None)

Plot points from sample n using matplotlib on PEPT-style axes

Given a mpl_toolkits.mplot3d.Axes3D axis, plots all points from sample number sampleN on the PEPT-style coordinates convention: x is parallel and horizontal to the screens, y is parallel and vertical to the screens, z is perpendicular to the screens. The mapping relative to the Cartesian coordinates would then be: (x, y, z) -> (z, x, y)

Parameters

ax : mpl_toolkits.mplot3D.Axes3D object

The 3D matplotlib-based axis for plotting.

n : int

The number of the sample to be plotted.

Returns

fig, ax : matplotlib figure and axes objects

 

Source code

def plot_points_sample_n_alt_axes(self, n, ax=None):
    '''Plot points from sample `n` using matplotlib on PEPT-style axes

    Given a **mpl_toolkits.mplot3d.Axes3D** axis, plots all points from
    sample number sampleN on the PEPT-style coordinates convention:
    **x** is *parallel and horizontal* to the screens, **y** is
    *parallel and vertical* to the screens, **z** is *perpendicular*
    to the screens. The mapping relative to the Cartesian coordinates
    would then be: (x, y, z) -> (z, x, y)

    Parameters
    ----------
    ax : mpl_toolkits.mplot3D.Axes3D object
        The 3D matplotlib-based axis for plotting.
    n : int
        The number of the sample to be plotted.

    Returns
    -------

    fig, ax : matplotlib figure and axes objects
    '''

    if ax == None:
        fig = plt.figure()
        ax  = fig.add_subplot(111, projection='3d')
    else:
        fig = plt.gcf()

    # Scatter x, y, z, [color]

    sample = self.sample_n(n)

    x = sample[:, 1]
    y = sample[:, 2]
    z = sample[:, 3]

    color = sample[:, -1]

    cmap = plt.cm.magma
    color_array = cmap(color)

    ax.scatter(z,x,y,c=color_array[0])

    return fig, ax

def points_sample_n_trace(self, n, size=2, color=None)

Get a Plotly trace for all points in sample n.

Returns a plotly.graph_objects.Scatter3d trace containing all points included in sample number n. Can then be passed to the plotly.graph_objects.figure.add_trace function or a PlotlyGrapher instance using the add_trace method.

Parameters

n : int

The number of the sample to be plotted.

Returns

plotly.graph_object.Scatter3d

A plotly.graph_objects.Scatter3d trace of all points in sample n.

Source code

def points_sample_n_trace(self, n, size = 2, color = None):
    '''Get a Plotly trace for all points in sample `n`.

    Returns a `plotly.graph_objects.Scatter3d` trace containing all points
    included in sample number `n`.
    Can then be passed to the `plotly.graph_objects.figure.add_trace`
    function or a `PlotlyGrapher` instance using the `add_trace` method.

    Parameters
    ----------
    n : int
        The number of the sample to be plotted.

    Returns
    -------
    plotly.graph_object.Scatter3d
        A `plotly.graph_objects.Scatter3d` trace of all points in sample `n`.

    '''

    sample = self.sample_n(n)
    trace = go.Scatter3d(
        x = sample[:, 1],
        y = sample[:, 2],
        z = sample[:, 3],
        mode = 'markers',
        marker = dict(
            size = size,
            color = color,
            opacity = 0.8
        )
    )

    return trace

def points_sample_n_trace_colorbar(self, n, size=2, colorbar_title=None)

Get a Plotly trace for all points in sample n, colour-coding the last column.

Returns a plotly.graph_objects.Scatter3d trace containing all points included in sample number n. Can then be passed to the plotly.graph_objects.figure.add_trace function or a PlotlyGrapher instance using the add_trace method.

Parameters

n : int

The number of the sample to be plotted.

Returns

plotly.graph_object.Scatter3d

A plotly.graph_objects.Scatter3d trace of all points in sample n.

Source code

def points_sample_n_trace_colorbar(self, n, size = 2, colorbar_title = None):
    '''Get a Plotly trace for all points in sample `n`, colour-coding the last column.

    Returns a `plotly.graph_objects.Scatter3d` trace containing all points
    included in sample number `n`.
    Can then be passed to the `plotly.graph_objects.figure.add_trace`
    function or a `PlotlyGrapher` instance using the `add_trace` method.

    Parameters
    ----------
    n : int
        The number of the sample to be plotted.

    Returns
    -------
    plotly.graph_object.Scatter3d
        A `plotly.graph_objects.Scatter3d` trace of all points in sample `n`.

    '''

    if colorbar_title != None:
        colorbar = dict(title = colorbar_title)
    else:
        colorbar = dict()

    sample = self.sample_n(n)
    trace = go.Scatter3d(
        x = sample[:, 1],
        y = sample[:, 2],
        z = sample[:, 3],
        mode = 'markers',
        marker = dict(
            size = size,
            color = sample[:, -1],
            colorscale = 'Magma',
            colorbar = colorbar,
            opacity = 0.8
        )
    )

    return trace

def points_trace(self, sample_indices=0, size=2, color=None, opacity=0.8, colorbar=False, colorbar_col=-1, colorbar_title=None)

Get a Plotly trace for all points in selected samples, with possible color-coding.

Returns a plotly.graph_objects.Scatter3d trace containing all points included in in the samples selected by sample_indices. sample_indices can be a single sample index (e.g. 0) or an iterable of indices (e.g. [1,5,6]). Can then be passed to the plotly.graph_objects.figure.add_trace function or a PlotlyGrapher instance using the add_trace method.

Parameters

sample_indices : int or iterable

The index or indices of the samples of LoRs. The default is 0 (the first sample).

size : float

The marker size of the points. The default is 2.

color : str or list-like

Can be a single color (e.g. "black", "rgb(122, 15, 241)") or a colorbar list. Is ignored if colorbar is set to True. For more information, check the Plotly documentation. The default is None.

opacity : float

The opacity of the lines, where 0 is transparent and 1 is fully opaque. The default is 0.8.

colorbar : bool

If set to True, will color-code the data in the sample column colorbar_col. Overrides color if set to True. The default is False.

colorbar_col : int

The column in the data samples that will be used to color the points. Only has an effect if colorbar is set to True. The default is -1 (the last column).

colorbar_title : str

If set, the colorbar will have this title above. The default is None.

Returns

plotly.graph_objs.Scatter3d

A Plotly trace of the points.

Source code

def points_trace(
    self,
    sample_indices = 0,
    size = 2,
    color = None,
    opacity = 0.8,
    colorbar = False,
    colorbar_col = -1,
    colorbar_title = None
):
    '''Get a Plotly trace for all points in selected samples, with possible color-coding.

    Returns a `plotly.graph_objects.Scatter3d` trace containing all points
    included in in the samples selected by `sample_indices`. `sample_indices`
    can be a single sample index (e.g. 0) or an iterable of indices (e.g.
    [1,5,6]).
    Can then be passed to the `plotly.graph_objects.figure.add_trace`
    function or a `PlotlyGrapher` instance using the `add_trace` method.

    Parameters
    ----------
    sample_indices : int or iterable
        The index or indices of the samples of LoRs. The default is 0 (the first sample).
    size : float
        The marker size of the points. The default is 2.
    color : str or list-like
        Can be a single color (e.g. "black", "rgb(122, 15, 241)") or a colorbar list.
        Is ignored if `colorbar` is set to True. For more information, check the Plotly
        documentation. The default is None.
    opacity : float
        The opacity of the lines, where 0 is transparent and 1 is fully
        opaque. The default is 0.8.
    colorbar : bool
        If set to True, will color-code the data in the sample column `colorbar_col`.
        Overrides `color` if set to True. The default is False.
    colorbar_col : int
        The column in the data samples that will be used to color the points. Only has
        an effect if `colorbar` is set to True. The default is -1 (the last column).
    colorbar_title : str
        If set, the colorbar will have this title above. The default is None.

    Returns
    -------
    plotly.graph_objs.Scatter3d
        A Plotly trace of the points.

    '''

    # Check if sample_indices is an iterable collection (list-like)
    # otherwise just "iterate" over the single number
    if not hasattr(sample_indices, "__iter__"):
        sample_indices = [sample_indices]

    coords_x = []
    coords_y = []
    coords_z = []

    marker = dict(
        size = size,
        color = color,
        opacity = opacity
    )

    if colorbar:
        marker['color'] = []
        marker.update(colorscale = "Magma")

        if colorbar_title is not None:
            marker.update(colorbar = dict(title = colorbar_title))

    # For each selected sample include all the needed coordinates
    for n in sample_indices:
        sample = self[n]

        coords_x.extend(sample[:, 1])
        coords_y.extend(sample[:, 2])
        coords_z.extend(sample[:, 3])

        if colorbar == True:
            marker['color'].extend(sample[:, colorbar_col])

    trace = go.Scatter3d(
        x = coords_x,
        y = coords_y,
        z = coords_z,
        mode = "markers",
        marker = marker
    )

    return trace

def sample_n(self, n)

Get sample number n (indexed from 1, i.e. n > 0)

Returns the lines from point_data included in sample number n. Samples are numbered starting from 1.

Parameters

n : int

The number of the sample required. Note that 1 <= n <= number_of_samples.

Returns

(M, N) numpy.ndarray A shallow copy of the points from point_data included in sample number n.

Raises

IndexError

If sample_size == 0, all data is returned as one single sample. Raised if n is not 1.

IndexError

If n > number_of_samples or n <= 0.

Source code

def sample_n(self, n):
    '''Get sample number n (indexed from 1, i.e. `n > 0`)

    Returns the lines from `point_data` included in sample number
    `n`. Samples are numbered starting from 1.

    Parameters
    ----------
    n : int
        The number of the sample required. Note that `1 <= n <=
        number_of_samples`.

    Returns
    -------
    (M, N) numpy.ndarray
        A shallow copy of the points from `point_data` included in
        sample number n.

    Raises
    ------
    IndexError
        If `sample_size == 0`, all data is returned as one single
        sample. Raised if `n` is not 1.
    IndexError
        If `n > number_of_samples` or `n <= 0`.

    '''
    if self._sample_size == 0:
        if n == 1:
            return self._point_data
        else:
            raise IndexError("\n\n[ERROR]: Trying to access a non-existent sample (samples indexed from 1): asked for sample number {}, when there is only 1 sample (sample_size == 0)\n".format(n))
    elif (n > self.number_of_samples) or n <= 0:
        raise IndexError("\n\n[ERROR]: Trying to access a non-existent sample (samples are indexed from 1): asked for sample number {}, when there are {} samples\n".format(n, self.number_of_samples))

    start_index = (n - 1) * (self._sample_size - self._overlap)
    return self._point_data[start_index:(start_index + self._sample_size)]

def to_csv(self, filepath, delimiter=' ', newline='\n')

Write point_data to a CSV file

    Write all points (and any extra data) stored in the class to a CSV file.

    Parameters
    ----------
        filepath : filename or file handle
            If filepath is a path (rather than file handle), it is relative
            to where python is called.
        delimiter : str, optional
            The delimiter between values. The default is two spaces '  ',
            such that numbers in the format '123,456.78' are well-understood.
        newline : str, optional
            The sequence of characters at the end of every line. The default
            is a new line '

'

Source code

def to_csv(self, filepath, delimiter = '  ', newline = '\n'):
    '''Write `point_data` to a CSV file

    Write all points (and any extra data) stored in the class to a CSV file.

    Parameters
    ----------
        filepath : filename or file handle
            If filepath is a path (rather than file handle), it is relative
            to where python is called.
        delimiter : str, optional
            The delimiter between values. The default is two spaces '  ',
            such that numbers in the format '123,456.78' are well-understood.
        newline : str, optional
            The sequence of characters at the end of every line. The default
            is a new line '\n'

    '''
    np.savetxt(filepath, self._point_data, delimiter = delimiter, newline = newline)