`spateo.tdr.models.models_migration`#

Submodules#

Package Contents#

Functions#

`_construct_arrow`(, direction, tuple, numpy.ndarray] =, ...)	Create a 3D arrow model.
`construct_arrow`(→ Tuple[pyvista.PolyData, Optional[str]])	Create a 3D arrow model.
`construct_arrows`(→ Tuple[pyvista.PolyData, Optional[str]])	Create multiple 3D arrows model.
`_construct_line`(, end_point, tuple, ...)	Create a 3D line model.
`construct_align_lines`(→ Tuple[pyvista.PolyData, ...)	Construct alignment lines between models after model alignment.
`construct_axis_line`(→ Tuple[pyvista.PolyData, ...)	Construct axis line.
`construct_line`(→ Tuple[pyvista.PolyData, Optional[str]])	Create a 3D line model.
`construct_lines`(→ Tuple[pyvista.PolyData, Optional[str]])	Create 3D lines model.
`generate_edges`(→ Tuple[numpy.ndarray, numpy.ndarray])
`construct_field`(→ Tuple[pyvista.PolyData, Optional[str]])	Create a 3D vector field arrows model.
`construct_field_streams`(model[, vf_key, ...])	Integrate a vector field to generate streamlines.
`construct_genesis`(→ Tuple[pyvista.MultiBlock, ...)	Reconstruction of cell-level cell developmental change model based on the cell fate prediction results. Here we only
`construct_genesis_X`(→ Tuple[pyvista.MultiBlock, ...)	Reconstruction of cell-level cell developmental change model based on the cell fate prediction results. Here we only
`construct_trajectory`(→ Tuple[Any, Optional[str]])	Reconstruction of cell developmental trajectory model based on cell fate prediction.
`construct_trajectory_X`(→ Tuple[Any, Optional[str]])	Reconstruction of cell developmental trajectory model.

spateo.tdr.models.models_migration._construct_arrow(start_point: list | tuple | numpy.ndarray = (0.0, 0.0, 0.0), direction: list | tuple | numpy.ndarray = (1.0, 0.0, 0.0), tip_length: float = 0.25, tip_radius: float = 0.1, tip_resolution: int = 20, shaft_radius: float = 0.05, shaft_resolution: int = 20, scale: str | float | None = 'auto') → pyvista.PolyData[source]#

Create a 3D arrow model.

Parameters:

start_point: Start location in [x, y, z] of the arrow.
direction: Direction the arrow points to in [x, y, z].
tip_length: Length of the tip.
tip_radius: Radius of the tip.
tip_resolution: Number of faces around the tip.
shaft_radius: Radius of the shaft.
shaft_resolution: Number of faces around the shaft.
scale: Scale factor of the entire object. 'auto' scales to length of direction array.

Returns:

Arrow model.

Create a 3D arrow model.

Parameters:

start_point: Start location in [x, y, z] of the arrow.
direction: Direction the arrow points to in [x, y, z].
arrow_scale: Scale factor of the entire object. ‘auto’ scales to length of direction array.
key_added: The key under which to add the labels.
label: The label of arrow model.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.
**kwargs: Additional parameters that will be passed to _construct_arrow function.

Returns:

Arrow model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

model

Create multiple 3D arrows model.

Parameters:

start_points: List of Start location in [x, y, z] of the arrows.
direction: Direction the arrows points to in [x, y, z].
arrows_scale: Scale factor of the entire object.
n_sampling: n_sampling is the number of coordinates to keep after sampling. If there are too many coordinates in start_points, the generated arrows model will be too complex and unsightly, so sampling is used to reduce the number of coordinates.
sampling_method: The method to sample data points, can be one of ['trn', 'kmeans', 'random'].
factor: Scale factor applied to scaling array.
key_added: The key under which to add the labels.
label: The label of arrows models.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.
**kwargs: Additional parameters that will be passed to _construct_arrow function.

Returns:

Arrows model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

model

spateo.tdr.models.models_migration._construct_line(start_point: list | tuple | numpy.ndarray = (-0.5, 0.0, 0.0), end_point: list | tuple | numpy.ndarray = (0.5, 0.0, 0.0)) → pyvista.PolyData[source]#

Create a 3D line model.

Parameters:

start_point: Start location in [x, y, z] of the line.
end_point: End location in [x, y, z] of the line.

Returns:

Line model.

Construct alignment lines between models after model alignment.

Parameters:

model1_points: Start location in model1 of the line.
model2_points: End location in model2 of the line.
key_added: The key under which to add the labels.
label: The label of alignment lines model.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.

Returns:

Alignment lines model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

model

spateo.tdr.models.models_migration.construct_axis_line(axis_points: numpy.ndarray, key_added: str = 'axis', label: str = 'axis_line', color: str = 'gainsboro', alpha: float | int | list | dict | numpy.ndarray = 1.0) → Tuple[pyvista.PolyData, str | None][source]#

Construct axis line.

Parameters:

axis_points: List of points defining an axis.
key_added: The key under which to add the labels.
label: The label of axis line model.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.

Returns:

Axis line model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

axis_line

Create a 3D line model.

Parameters:

start_point: Start location in [x, y, z] of the line.
end_point: End location in [x, y, z] of the line.
key_added: The key under which to add the labels.
label: The label of line model.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.

Returns:

Line model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

model

Create 3D lines model.

Parameters:

points: List of points.
edges: The edges between points.
key_added: The key under which to add the labels.
label: The label of lines model.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.

Returns:

Lines model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

model

spateo.tdr.models.models_migration.generate_edges(points1: numpy.ndarray, points2: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray][source]#

spateo.tdr.models.models_migration.construct_field(model: pyvista.PolyData, vf_key: str = 'VecFld_morpho', arrows_scale_key: str | None = None, n_sampling: int | None = None, sampling_method: str = 'trn', factor: float = 1.0, key_added: str = 'v_arrows', label: str | list | numpy.ndarray = 'vector field', color: str | list | dict | numpy.ndarray = 'gainsboro', alpha: float = 1.0, **kwargs) → Tuple[pyvista.PolyData, str | None][source]#

Create a 3D vector field arrows model.

Parameters:

model: A model that provides coordinate information and vector information for constructing vector field models.
vf_key: The key under which are the vector information.
arrows_scale_key: The key under which are scale factor of the entire object.
n_sampling: n_sampling is the number of coordinates to keep after sampling. If there are too many coordinates in start_points, the generated arrows model will be too complex and unsightly, so sampling is used to reduce the number of coordinates.
sampling_method: The method to sample data points, can be one of ['trn', 'kmeans', 'random'].
factor: Scale factor applied to scaling array.
key_added: The key under which to add the labels.
label: The label of arrows models.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.
**kwargs: Additional parameters that will be passed to construct_arrows function.

Returns:

A 3D vector field arrows model. plot_cmap: Recommended colormap parameter values for plotting.

spateo.tdr.models.models_migration.construct_field_streams(model: pyvista.PolyData, vf_key: str = 'VecFld_morpho', source_center: Tuple[float] | None = None, source_radius: float | None = None, tip_factor: int | float = 10, tip_radius: float = 0.2, key_added: str = 'v_streams', label: str | list | numpy.ndarray = 'vector field', stream_color: str = 'gainsboro', tip_color: str = 'orangered', alpha: float = 1.0, **kwargs)[source]#

Integrate a vector field to generate streamlines.

Parameters:

model: A model that provides coordinate information and vector information for constructing vector field models.
vf_key: The key under which are the active vector field information.
source_center: Length 3 tuple of floats defining the center of the source particles. Defaults to the center of the dataset.
source_radius: Float radius of the source particle cloud. Defaults to one-tenth of the diagonal of the dataset’s spatial extent.
tip_factor: Scale factor applied to scaling the tips.
tip_radius: Radius of the tips.
key_added: The key under which to add the labels.
label: The label of arrows models.
stream_color: Color to use for plotting streamlines.
tip_color: Color to use for plotting tips.
alpha: The opacity of the color to use for plotting model.
**kwargs: Additional parameters that will be passed to streamlines function.

Returns:

3D vector field streamlines model. src: The source particles as pyvista.PolyData as well as the streamlines. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

streams_model

Reconstruction of cell-level cell developmental change model based on the cell fate prediction results. Here we only need to enter the three-dimensional coordinates of the cells at different developmental stages.

Parameters:

adata: AnnData object that contains the fate prediction in the .uns attribute.
fate_key: The key under which are the active fate information.
n_steps: The number of times steps fate prediction will take.
logspace: Whether or to sample time points linearly on log space. If not, the sorted unique set of all times points from all cell states’ fate prediction will be used and then evenly sampled up to n_steps time points.
t_end: The length of the time period from which to predict cell state forward or backward over time.
key_added: The key under which to add the labels.
label: The label of cell developmental change model. If label == None, the label will be automatically generated.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.

Returns:

A MultiBlock contains cell models for all stages. plot_cmap: Recommended colormap parameter values for plotting.

Parameters:

stages_X: The three-dimensional coordinates of the cells at different developmental stages.
n_spacing: Subdivided into n_spacing time points between two periods.
key_added: The key under which to add the labels.
label: The label of cell developmental change model. If label == None, the label will be automatically generated.
color: Color to use for plotting model.
alpha: The opacity of the color to use for plotting model.

Returns:

A MultiBlock contains cell models for all stages. plot_cmap: Recommended colormap parameter values for plotting.

spateo.tdr.models.models_migration.construct_trajectory(adata: anndata.AnnData, fate_key: str = 'fate_develop', n_sampling: int | None = None, sampling_method: str = 'trn', key_added: str = 'trajectory', label: str | list | numpy.ndarray | None = None, tip_factor: int | float = 5, tip_radius: float = 0.2, trajectory_color: str | list | dict = 'gainsboro', tip_color: str | list | dict = 'orangered', alpha: float = 1.0) → Tuple[Any, str | None][source]#

Reconstruction of cell developmental trajectory model based on cell fate prediction.

Parameters:

adata: AnnData object that contains the fate prediction in the .uns attribute.
fate_key: The key under which are the active fate information.
n_sampling: n_sampling is the number of coordinates to keep after sampling. If there are too many coordinates in start_points, the generated arrows model will be too complex and unsightly, so sampling is used to reduce the number of coordinates.
sampling_method: The method to sample data points, can be one of ['trn', 'kmeans', 'random'].
key_added: The key under which to add the labels.
label: The label of trajectory model.
tip_factor: Scale factor applied to scaling the tips.
tip_radius: Radius of the tips.
trajectory_color: Color to use for plotting trajectory model.
tip_color: Color to use for plotting tips.
alpha: The opacity of the color to use for plotting model.

Returns:

3D cell developmental trajectory model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

trajectory_model

Reconstruction of cell developmental trajectory model.

Parameters:

cells_states: Three-dimensional coordinates of all cells at all times points.
init_states: Three-dimensional coordinates of all cells at the starting time point.
n_sampling: n_sampling is the number of coordinates to keep after sampling. If there are too many coordinates in start_points, the generated arrows model will be too complex and unsightly, so sampling is used to reduce the number of coordinates.
sampling_method: The method to sample data points, can be one of ['trn', 'kmeans', 'random'].
key_added: The key under which to add the labels.
label: The label of trajectory model.
tip_factor: Scale factor applied to scaling the tips.
tip_radius: Radius of the tips.
trajectory_color: Color to use for plotting trajectory model.
tip_color: Color to use for plotting tips.
alpha: The opacity of the color to use for plotting model.

Returns:

3D cell developmental trajectory model. plot_cmap: Recommended colormap parameter values for plotting.

Return type:

trajectory_model

spateo.tdr.models.models_migration#

Submodules#

Package Contents#

Functions#

`spateo.tdr.models.models_migration`#