segger.data.io¶
MerscopeKeys ¶
Bases: Enum
Keys for MERSCOPE data (Vizgen platform).
MerscopeSample ¶
MerscopeSample(transcripts_df=None, transcripts_radius=10, boundaries_graph=False)
Bases: SpatialTranscriptomicsSample
Source code in src/segger/data/io.py
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filter_transcripts ¶
filter_transcripts(transcripts_df, min_qv=20.0)
Filters transcripts based on specific criteria for Merscope using Dask.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_df |
dd.DataFrame The Dask DataFrame containing transcript data. |
required | |
min_qv |
float, optional The minimum quality value threshold for filtering transcripts. |
20.0
|
Returns:
Type | Description |
---|---|
DataFrame
|
dd.DataFrame The filtered Dask DataFrame. |
Source code in src/segger/data/io.py
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SpatialTranscriptomicsDataset ¶
SpatialTranscriptomicsDataset(root, transform=None, pre_transform=None, pre_filter=None)
Bases: InMemoryDataset
A dataset class for handling SpatialTranscriptomics spatial transcriptomics data.
Attributes:
Name | Type | Description |
---|---|---|
root |
str
|
The root directory where the dataset is stored. |
transform |
callable
|
A function/transform that takes in a Data object and returns a transformed version. |
pre_transform |
callable
|
A function/transform that takes in a Data object and returns a transformed version. |
pre_filter |
callable
|
A function that takes in a Data object and returns a boolean indicating whether to keep it. |
Initialize the SpatialTranscriptomicsDataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root |
str
|
Root directory where the dataset is stored. |
required |
transform |
callable
|
A function/transform that takes in a Data object and returns a transformed version. Defaults to None. |
None
|
pre_transform |
callable
|
A function/transform that takes in a Data object and returns a transformed version. Defaults to None. |
None
|
pre_filter |
callable
|
A function that takes in a Data object and returns a boolean indicating whether to keep it. Defaults to None. |
None
|
Source code in src/segger/data/utils.py
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processed_file_names
property
¶
processed_file_names
Return a list of processed file names in the processed directory.
Returns:
Type | Description |
---|---|
List[str]
|
List[str]: List of processed file names. |
raw_file_names
property
¶
raw_file_names
Return a list of raw file names in the raw directory.
Returns:
Type | Description |
---|---|
List[str]
|
List[str]: List of raw file names. |
download ¶
download()
Download the raw data. This method should be overridden if you need to download the data.
Source code in src/segger/data/utils.py
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get ¶
get(idx)
Get a processed data object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
idx |
int
|
Index of the data object to retrieve. |
required |
Returns:
Name | Type | Description |
---|---|---|
Data |
Data
|
The processed data object. |
Source code in src/segger/data/utils.py
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len ¶
len()
Return the number of processed files.
Returns:
Name | Type | Description |
---|---|---|
int |
int
|
Number of processed files. |
Source code in src/segger/data/utils.py
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process ¶
process()
Process the raw data and save it to the processed directory. This method should be overridden if you need to process the data.
Source code in src/segger/data/utils.py
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SpatialTranscriptomicsKeys ¶
Bases: Enum
Unified keys for spatial transcriptomics data, supporting multiple platforms.
SpatialTranscriptomicsSample ¶
SpatialTranscriptomicsSample(transcripts_df=None, transcripts_radius=10, boundaries_graph=False, keys=None, verbose=True)
Bases: ABC
Initialize the SpatialTranscriptomicsSample class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_df |
DataFrame
|
A DataFrame containing transcript data. |
None
|
transcripts_radius |
int
|
Radius for transcripts in the analysis. |
10
|
boundaries_graph |
bool
|
Whether to include boundaries (e.g., nucleus, cell) graph information. |
False
|
keys |
Dict
|
The enum class containing key mappings specific to the dataset. |
None
|
Source code in src/segger/data/io.py
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build_pyg_data_from_tile ¶
build_pyg_data_from_tile(boundaries_df, transcripts_df, r_tx=5.0, k_tx=3, method='kd_tree', gpu=False, workers=1, scale_boundaries=1.0)
Builds PyG data from a tile of boundaries and transcripts data using Dask utilities for efficient processing.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
boundaries_df |
DataFrame
|
Dask DataFrame containing boundaries data (e.g., nucleus, cell). |
required |
transcripts_df |
DataFrame
|
Dask DataFrame containing transcripts data. |
required |
r_tx |
float
|
Radius for building the transcript-to-transcript graph. |
5.0
|
k_tx |
int
|
Number of nearest neighbors for the tx-tx graph. |
3
|
method |
str
|
Method for computing edge indices (e.g., 'kd_tree', 'faiss'). |
'kd_tree'
|
gpu |
bool
|
Whether to use GPU acceleration for edge index computation. |
False
|
workers |
int
|
Number of workers to use for parallel processing. |
1
|
scale_boundaries |
float
|
The factor by which to scale the boundary polygons. Default is 1.0. |
1.0
|
Returns:
Name | Type | Description |
---|---|---|
HeteroData |
HeteroData
|
PyG Heterogeneous Data object. |
Source code in src/segger/data/io.py
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compute_boundaries_geometries ¶
compute_boundaries_geometries(boundaries_df=None, polygons_gdf=None, scale_factor=1.0, area=True, convexity=True, elongation=True, circularity=True)
Computes geometries for boundaries (e.g., nuclei, cells) from the dataframe using Dask.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
boundaries_df |
DataFrame
|
The dataframe containing boundaries data. Required if polygons_gdf is not provided. |
None
|
polygons_gdf |
GeoDataFrame
|
Precomputed Dask GeoDataFrame containing boundary polygons. If None, will compute from boundaries_df. |
None
|
scale_factor |
float
|
The factor by which to scale the polygons (default is 1.0). |
1.0
|
area |
bool
|
Whether to compute area. |
True
|
convexity |
bool
|
Whether to compute convexity. |
True
|
elongation |
bool
|
Whether to compute elongation. |
True
|
circularity |
bool
|
Whether to compute circularity. |
True
|
Returns:
Type | Description |
---|---|
GeoDataFrame
|
dgpd.GeoDataFrame: A GeoDataFrame containing computed geometries. |
Source code in src/segger/data/io.py
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compute_transcript_overlap_with_boundaries ¶
compute_transcript_overlap_with_boundaries(transcripts_df, boundaries_df=None, polygons_gdf=None, scale_factor=1.0)
Computes the overlap of transcript locations with scaled boundary polygons and assigns corresponding cell IDs to the transcripts using Dask.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_df |
DataFrame
|
Dask DataFrame containing transcript data. |
required |
boundaries_df |
DataFrame
|
Dask DataFrame containing boundary data. Required if polygons_gdf is not provided. |
None
|
polygons_gdf |
GeoDataFrame
|
Precomputed Dask GeoDataFrame containing boundary polygons. If None, will compute from boundaries_df. |
None
|
scale_factor |
float
|
The factor by which to scale the boundary polygons. Default is 1.0. |
1.0
|
Returns:
Type | Description |
---|---|
DataFrame
|
dd.DataFrame: The updated DataFrame with overlap information and assigned cell IDs. |
Source code in src/segger/data/io.py
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create_scaled_polygon
staticmethod
¶
create_scaled_polygon(group, scale_factor, keys)
Static method to create and scale a polygon from boundary vertices and return a GeoDataFrame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
group |
DataFrame
|
Group of boundary coordinates (for a specific cell). |
required |
scale_factor |
float
|
The factor by which to scale the polygons. |
required |
keys |
Dict or Enum
|
A collection of keys to access column names for 'cell_id', 'vertex_x', and 'vertex_y'. |
required |
Returns:
Type | Description |
---|---|
GeoDataFrame
|
gpd.GeoDataFrame: A GeoDataFrame containing the scaled Polygon and cell_id. |
Source code in src/segger/data/io.py
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filter_transcripts
abstractmethod
¶
filter_transcripts(transcripts_df, min_qv=20.0)
Abstract method to filter transcripts based on dataset-specific criteria.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_df |
DataFrame
|
The dataframe containing transcript data. |
required |
min_qv |
float
|
The minimum quality value threshold for filtering transcripts. |
20.0
|
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: The filtered dataframe. |
Source code in src/segger/data/io.py
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generate_and_scale_polygons ¶
generate_and_scale_polygons(boundaries_df, scale_factor=1.0)
Generate and scale polygons from boundary coordinates using Dask. Keeps class structure intact by using static method for the core polygon generation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
boundaries_df |
DataFrame
|
DataFrame containing boundary coordinates. |
required |
scale_factor |
float
|
The factor by which to scale the polygons (default is 1.0). |
1.0
|
Returns:
Type | Description |
---|---|
GeoDataFrame
|
dgpd.GeoDataFrame: A GeoDataFrame containing scaled Polygon objects and their centroids. |
Source code in src/segger/data/io.py
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load_boundaries ¶
load_boundaries(path, file_format='parquet', x_min=None, x_max=None, y_min=None, y_max=None)
Load boundaries data lazily using Dask, filtering by the specified bounding box.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path |
Path
|
Path to the boundaries file. |
required |
file_format |
str
|
Format of the file to load. Only 'parquet' is supported in this refactor. |
'parquet'
|
x_min |
float
|
Minimum X-coordinate for the bounding box. |
None
|
x_max |
float
|
Maximum X-coordinate for the bounding box. |
None
|
y_min |
float
|
Minimum Y-coordinate for the bounding box. |
None
|
y_max |
float
|
Maximum Y-coordinate for the bounding box. |
None
|
Returns:
Type | Description |
---|---|
DataFrame
|
dd.DataFrame: The filtered boundaries DataFrame. |
Source code in src/segger/data/io.py
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load_transcripts ¶
load_transcripts(base_path=None, sample=None, transcripts_filename=None, path=None, file_format='parquet', x_min=None, x_max=None, y_min=None, y_max=None)
Load transcripts from a Parquet file using Dask for efficient chunked processing, only within the specified bounding box, and return the filtered DataFrame with integer token embeddings.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
base_path |
Path
|
The base directory path where samples are stored. |
None
|
sample |
str
|
The sample name or identifier. |
None
|
transcripts_filename |
str
|
The filename of the transcripts file (default is derived from the dataset keys). |
None
|
path |
Path
|
Specific path to the transcripts file. |
None
|
file_format |
str
|
Format of the file to load (default is 'parquet'). |
'parquet'
|
x_min |
float
|
Minimum X-coordinate for the bounding box. |
None
|
x_max |
float
|
Maximum X-coordinate for the bounding box. |
None
|
y_min |
float
|
Minimum Y-coordinate for the bounding box. |
None
|
y_max |
float
|
Maximum Y-coordinate for the bounding box. |
None
|
Returns:
Type | Description |
---|---|
DataFrame
|
dd.DataFrame: The filtered transcripts DataFrame. |
Source code in src/segger/data/io.py
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save_dataset_for_segger ¶
save_dataset_for_segger(processed_dir, x_size=1000, y_size=1000, d_x=900, d_y=900, margin_x=None, margin_y=None, compute_labels=True, r_tx=5, k_tx=3, val_prob=0.1, test_prob=0.2, neg_sampling_ratio_approx=5, sampling_rate=1, num_workers=1, scale_boundaries=1.0, method='kd_tree', gpu=False, workers=1)
Saves the dataset for Segger in a processed format using Dask for parallel and lazy processing.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
processed_dir |
Path
|
Directory to save the processed dataset. |
required |
x_size |
float
|
Width of each tile. |
1000
|
y_size |
float
|
Height of each tile. |
1000
|
d_x |
float
|
Step size in the x direction for tiles. |
900
|
d_y |
float
|
Step size in the y direction for tiles. |
900
|
margin_x |
float
|
Margin in the x direction to include transcripts. |
None
|
margin_y |
float
|
Margin in the y direction to include transcripts. |
None
|
compute_labels |
bool
|
Whether to compute edge labels for tx_belongs_bd edges. |
True
|
r_tx |
float
|
Radius for building the transcript-to-transcript graph. |
5
|
k_tx |
int
|
Number of nearest neighbors for the tx-tx graph. |
3
|
val_prob |
float
|
Probability of assigning a tile to the validation set. |
0.1
|
test_prob |
float
|
Probability of assigning a tile to the test set. |
0.2
|
neg_sampling_ratio_approx |
float
|
Approximate ratio of negative samples. |
5
|
sampling_rate |
float
|
Rate of sampling tiles. |
1
|
num_workers |
int
|
Number of workers to use for parallel processing. |
1
|
scale_boundaries |
float
|
The factor by which to scale the boundary polygons. Default is 1.0. |
1.0
|
method |
str
|
Method for computing edge indices (e.g., 'kd_tree', 'faiss'). |
'kd_tree'
|
gpu |
bool
|
Whether to use GPU acceleration for edge index computation. |
False
|
workers |
int
|
Number of workers to use to compute the neighborhood graph (per tile). |
1
|
Source code in src/segger/data/io.py
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set_embedding ¶
set_embedding(embedding_name)
Set the current embedding type for the transcripts.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
embedding_name |
str The name of the embedding to use. |
required |
Source code in src/segger/data/io.py
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set_file_paths ¶
set_file_paths(transcripts_path, boundaries_path)
Set the paths for the transcript and boundary files.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_path |
Path
|
Path to the Parquet file containing transcripts data. |
required |
boundaries_path |
Path
|
Path to the Parquet file containing boundaries data. |
required |
Source code in src/segger/data/io.py
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set_metadata ¶
set_metadata()
Set metadata for the transcript dataset, including bounding box limits and unique gene names, without reading the entire Parquet file. Additionally, return integer tokens for unique gene names instead of one-hot encodings and store the lookup table for later mapping.
Source code in src/segger/data/io.py
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XeniumKeys ¶
Bases: Enum
Keys for 10X Genomics Xenium formatted dataset.
XeniumSample ¶
XeniumSample(transcripts_df=None, transcripts_radius=10, boundaries_graph=False, verbose=True)
Bases: SpatialTranscriptomicsSample
Source code in src/segger/data/io.py
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filter_transcripts ¶
filter_transcripts(transcripts_df, min_qv=20.0)
Filters transcripts based on quality value and removes unwanted transcripts for Xenium using Dask.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_df |
DataFrame
|
The Dask DataFrame containing transcript data. |
required |
min_qv |
float
|
The minimum quality value threshold for filtering transcripts. |
20.0
|
Returns:
Type | Description |
---|---|
DataFrame
|
dd.DataFrame: The filtered Dask DataFrame. |
Source code in src/segger/data/io.py
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calculate_gene_celltype_abundance_embedding ¶
calculate_gene_celltype_abundance_embedding(adata, celltype_column)
Calculate the cell type abundance embedding for each gene based on the percentage of cells in each cell type that express the gene (non-zero expression).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
adata |
AnnData
|
An AnnData object containing gene expression data and cell type information. |
required |
celltype_column |
str
|
The column name in |
required |
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A DataFrame where rows are genes and columns are cell types, with each value representing the percentage of cells in that cell type expressing the gene. |
Example
adata = AnnData(...) # Load your scRNA-seq AnnData object celltype_column = 'celltype_major' abundance_df = calculate_gene_celltype_abundance_embedding(adata, celltype_column) abundance_df.head()
Source code in src/segger/data/utils.py
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compute_transcript_metrics ¶
compute_transcript_metrics(df, qv_threshold=30, cell_id_col='cell_id')
Computes various metrics for a given dataframe of transcript data filtered by quality value threshold.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
df |
DataFrame
|
The dataframe containing transcript data. |
required |
qv_threshold |
float
|
The quality value threshold for filtering transcripts. |
30
|
cell_id_col |
str
|
The name of the column representing the cell ID. |
'cell_id'
|
Returns:
Type | Description |
---|---|
Dict[str, Any]
|
Dict[str, Any]: A dictionary containing various transcript metrics: - 'percent_assigned' (float): The percentage of assigned transcripts. - 'percent_cytoplasmic' (float): The percentage of cytoplasmic transcripts among assigned transcripts. - 'percent_nucleus' (float): The percentage of nucleus transcripts among assigned transcripts. - 'percent_non_assigned_cytoplasmic' (float): The percentage of non-assigned cytoplasmic transcripts. - 'gene_metrics' (pd.DataFrame): A dataframe containing gene-level metrics. |
Source code in src/segger/data/utils.py
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create_anndata ¶
create_anndata(df, panel_df=None, min_transcripts=5, cell_id_col='cell_id', qv_threshold=30, min_cell_area=10.0, max_cell_area=1000.0)
Generates an AnnData object from a dataframe of segmented transcriptomics data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
df |
DataFrame
|
The dataframe containing segmented transcriptomics data. |
required |
panel_df |
Optional[DataFrame]
|
The dataframe containing panel information. |
None
|
min_transcripts |
int
|
The minimum number of transcripts required for a cell to be included. |
5
|
cell_id_col |
str
|
The column name representing the cell ID in the input dataframe. |
'cell_id'
|
qv_threshold |
float
|
The quality value threshold for filtering transcripts. |
30
|
min_cell_area |
float
|
The minimum cell area to include a cell. |
10.0
|
max_cell_area |
float
|
The maximum cell area to include a cell. |
1000.0
|
Returns:
Type | Description |
---|---|
AnnData
|
ad.AnnData: The generated AnnData object containing the transcriptomics data and metadata. |
Source code in src/segger/data/utils.py
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filter_transcripts ¶
filter_transcripts(transcripts_df, min_qv=20.0)
Filters transcripts based on quality value and removes unwanted transcripts.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
transcripts_df |
DataFrame
|
The dataframe containing transcript data. |
required |
min_qv |
float
|
The minimum quality value threshold for filtering transcripts. |
20.0
|
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: The filtered dataframe. |
Source code in src/segger/data/utils.py
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get_edge_index ¶
get_edge_index(coords_1, coords_2, k=5, dist=10, method='kd_tree', gpu=False, workers=1)
Computes edge indices using various methods (KD-Tree, FAISS, RAPIDS cuML, cuGraph, or cuSpatial).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates. |
required |
coords_2 |
ndarray
|
Second set of coordinates. |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
method |
str
|
The method to use ('kd_tree', 'faiss', 'rapids', 'cugraph', 'cuspatial'). |
'kd_tree'
|
gpu |
bool
|
Whether to use GPU acceleration (applicable for FAISS). |
False
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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get_edge_index_cugraph ¶
get_edge_index_cugraph(coords_1, coords_2, k=5, dist=10)
Computes edge indices using RAPIDS cuGraph.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates. |
required |
coords_2 |
ndarray
|
Second set of coordinates. |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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|
get_edge_index_cuspatial ¶
get_edge_index_cuspatial(coords_1, coords_2, k=5, dist=10)
Computes edge indices using cuSpatial's spatial join functionality.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates (2D). |
required |
coords_2 |
ndarray
|
Second set of coordinates (2D). |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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get_edge_index_faiss ¶
get_edge_index_faiss(coords_1, coords_2, k=5, dist=10, gpu=False)
Computes edge indices using FAISS.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates. |
required |
coords_2 |
ndarray
|
Second set of coordinates. |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
gpu |
bool
|
Whether to use GPU acceleration. |
False
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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get_edge_index_hnsw ¶
get_edge_index_hnsw(coords_1, coords_2, k=5, dist=10)
Computes edge indices using the HNSW algorithm.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates. |
required |
coords_2 |
ndarray
|
Second set of coordinates. |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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get_edge_index_kdtree ¶
get_edge_index_kdtree(coords_1, coords_2, k=5, dist=10, workers=1)
Computes edge indices using KDTree.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates. |
required |
coords_2 |
ndarray
|
Second set of coordinates. |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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get_edge_index_rapids ¶
get_edge_index_rapids(coords_1, coords_2, k=5, dist=10)
Computes edge indices using RAPIDS cuML.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords_1 |
ndarray
|
First set of coordinates. |
required |
coords_2 |
ndarray
|
Second set of coordinates. |
required |
k |
int
|
Number of nearest neighbors. |
5
|
dist |
int
|
Distance threshold. |
10
|
Returns:
Type | Description |
---|---|
Tensor
|
torch.Tensor: Edge indices. |
Source code in src/segger/data/utils.py
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get_xy_extents ¶
get_xy_extents(filepath, x, y)
Get the bounding box of the x and y coordinates from a Parquet file.
Parameters¶
filepath : str The path to the Parquet file. x : str The name of the column representing the x-coordinate. y : str The name of the column representing the y-coordinate.
Returns¶
shapely.Polygon A polygon representing the bounding box of the x and y coordinates.
Source code in src/segger/data/utils.py
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