Processing Module¶
gigaspatial.processing
¶
DataStore
¶
Bases: ABC
Abstract base class defining the interface for data store implementations. This class serves as a parent for both local and cloud-based storage solutions.
Source code in gigaspatial/core/io/data_store.py
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file_exists(path)
abstractmethod
¶
Check if a file exists in the data store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Path to check | required |
Returns:
Type | Description |
---|---|
bool | True if file exists, False otherwise |
is_dir(path)
abstractmethod
¶
Check if path points to a directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Path to check | required |
Returns:
Type | Description |
---|---|
bool | True if path is a directory, False otherwise |
is_file(path)
abstractmethod
¶
Check if path points to a file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Path to check | required |
Returns:
Type | Description |
---|---|
bool | True if path is a file, False otherwise |
list_files(path)
abstractmethod
¶
List all files in a directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Directory path to list | required |
Returns:
Type | Description |
---|---|
List[str] | List of file paths in the directory |
open(file, mode='r')
abstractmethod
¶
Context manager for file operations.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
file | str | Path to the file | required |
mode | str | File mode ('r', 'w', 'rb', 'wb') | 'r' |
Yields:
Type | Description |
---|---|
Union[str, bytes] | File-like object |
Raises:
Type | Description |
---|---|
IOError | If file cannot be opened |
Source code in gigaspatial/core/io/data_store.py
read_file(path)
abstractmethod
¶
Read contents of a file from the data store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Path to the file to read | required |
Returns:
Type | Description |
---|---|
Any | Contents of the file |
Raises:
Type | Description |
---|---|
IOError | If file cannot be read |
Source code in gigaspatial/core/io/data_store.py
remove(path)
abstractmethod
¶
Remove a file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Path to the file to remove | required |
Raises:
Type | Description |
---|---|
IOError | If file cannot be removed |
rmdir(dir)
abstractmethod
¶
Remove a directory and all its contents.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dir | str | Path to the directory to remove | required |
Raises:
Type | Description |
---|---|
IOError | If directory cannot be removed |
walk(top)
abstractmethod
¶
Walk through directory tree, similar to os.walk().
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top | str | Starting directory for the walk | required |
Returns:
Type | Description |
---|---|
Generator | Generator yielding tuples of (dirpath, dirnames, filenames) |
Source code in gigaspatial/core/io/data_store.py
write_file(path, data)
abstractmethod
¶
Write data to a file in the data store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path | str | Path where to write the file | required |
data | Any | Data to write to the file | required |
Raises:
Type | Description |
---|---|
IOError | If file cannot be written |
Source code in gigaspatial/core/io/data_store.py
LocalDataStore
¶
Bases: DataStore
Implementation for local filesystem storage.
Source code in gigaspatial/core/io/local_data_store.py
copy_file(src, dst)
¶
Copy a file from src to dst.
Source code in gigaspatial/core/io/local_data_store.py
TifProcessor
¶
A class to handle tif data processing, supporting single-band, RGB, RGBA, and multi-band data. Can merge multiple rasters into one during initialization.
Source code in gigaspatial/processing/tif_processor.py
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bounds
property
¶
Get the bounds of the TIF file
count: int
property
¶
Get the band count from the TIF file
crs
property
¶
Get the coordinate reference system from the TIF file
dtype
property
¶
Get the data types from the TIF file
is_merged: bool
property
¶
Check if this processor was created from multiple rasters.
nodata: int
property
¶
Get the value representing no data in the rasters
resolution: Tuple[float, float]
property
¶
Get the x and y resolution (pixel width and height or pixel size) from the TIF file
source_count: int
property
¶
Get the number of source rasters.
transform
property
¶
Get the transform from the TIF file
x_transform: float
property
¶
Get the x transform from the TIF file
y_transform: float
property
¶
Get the y transform from the TIF file
__del__()
¶
__exit__(exc_type, exc_value, traceback)
¶
__post_init__()
¶
Validate inputs, merge rasters if needed, and set up logging.
Source code in gigaspatial/processing/tif_processor.py
cleanup()
¶
Explicit cleanup method for better control.
clip_to_bounds(bounds, bounds_crs=None, return_clipped_processor=True)
¶
Clip raster to rectangular bounds.
Parameters:¶
bounds : tuple Bounding box as (minx, miny, maxx, maxy) bounds_crs : str, optional CRS of the bounds. If None, assumes same as raster CRS return_clipped_processor : bool, default True If True, returns new TifProcessor, else returns (array, transform, metadata)
Returns:¶
TifProcessor or tuple Either new TifProcessor instance or (array, transform, metadata) tuple
Source code in gigaspatial/processing/tif_processor.py
clip_to_geometry(geometry, crop=True, all_touched=True, invert=False, nodata=None, pad=False, pad_width=0.5, return_clipped_processor=True)
¶
Clip raster to geometry boundaries.
Parameters:¶
geometry : various Geometry to clip to. Can be: - Shapely Polygon or MultiPolygon - GeoDataFrame or GeoSeries - List of GeoJSON-like dicts - Single GeoJSON-like dict crop : bool, default True Whether to crop the raster to the extent of the geometry all_touched : bool, default True Include pixels that touch the geometry boundary invert : bool, default False If True, mask pixels inside geometry instead of outside nodata : int or float, optional Value to use for masked pixels. If None, uses raster's nodata value pad : bool, default False Pad geometry by half pixel before clipping pad_width : float, default 0.5 Width of padding in pixels if pad=True return_clipped_processor : bool, default True If True, returns new TifProcessor with clipped data If False, returns (clipped_array, transform, metadata)
Returns:¶
TifProcessor or tuple Either new TifProcessor instance or (array, transform, metadata) tuple
Source code in gigaspatial/processing/tif_processor.py
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get_raster_info()
¶
Get comprehensive raster information.
Source code in gigaspatial/processing/tif_processor.py
open_dataset()
¶
Context manager for accessing the dataset, handling temporary reprojected files.
Source code in gigaspatial/processing/tif_processor.py
reproject_to(target_crs, output_path=None, resampling_method=None, resolution=None)
¶
Reprojects the current raster to a new CRS and optionally saves it.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
target_crs | str | The CRS to reproject to (e.g., "EPSG:4326"). | required |
output_path | Optional[Union[str, Path]] | The path to save the reprojected raster. If None, it is saved to a temporary file. | None |
resampling_method | Optional[Resampling] | The resampling method to use. | None |
resolution | Optional[Tuple[float, float]] | The target resolution (pixel size) in the new CRS. | None |
Source code in gigaspatial/processing/tif_processor.py
sample_by_polygons(polygon_list, stat='mean')
¶
Sample raster values by polygons and compute statistic(s) for each polygon.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
polygon_list | List of shapely Polygon or MultiPolygon objects. | required | |
stat | Union[str, Callable, List[Union[str, Callable]]] | Statistic(s) to compute. Can be: - Single string: 'mean', 'median', 'sum', 'min', 'max', 'std', 'count' - Single callable: custom function that takes array and returns scalar - List of strings/callables: multiple statistics to compute | 'mean' |
Returns:
Type | Description |
---|---|
If single stat: np.ndarray of computed statistics for each polygon | |
If multiple stats: List of dictionaries with stat names as keys |
Source code in gigaspatial/processing/tif_processor.py
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sample_by_polygons_batched(polygon_list, stat='mean', batch_size=100, n_workers=4, show_progress=True, check_memory=True, **kwargs)
¶
Sample raster values by polygons in parallel using batching.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
polygon_list | List[Union[Polygon, MultiPolygon]] | List of Shapely Polygon or MultiPolygon objects | required |
stat | Union[str, Callable] | Statistic to compute | 'mean' |
batch_size | int | Number of polygons per batch | 100 |
n_workers | int | Number of worker processes | 4 |
show_progress | bool | Whether to display progress bar | True |
check_memory | bool | Whether to check memory before operation | True |
**kwargs | Additional arguments | {} |
Returns:
Type | Description |
---|---|
ndarray | np.ndarray of statistics for each polygon |
Source code in gigaspatial/processing/tif_processor.py
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to_dataframe(drop_nodata=True, check_memory=True, **kwargs)
¶
Convert raster to DataFrame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
drop_nodata | Whether to drop nodata values | True | |
check_memory | Whether to check memory before operation (default True) | True | |
**kwargs | Additional arguments | {} |
Returns:
Type | Description |
---|---|
DataFrame | pd.DataFrame with raster data |
Source code in gigaspatial/processing/tif_processor.py
to_dataframe_chunked(drop_nodata=True, chunk_size=None, target_memory_mb=500, **kwargs)
¶
Convert raster to DataFrame using chunked processing for memory efficiency.
Automatically routes to the appropriate chunked method based on mode. Chunk size is automatically calculated based on target memory usage.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
drop_nodata | Whether to drop nodata values | True | |
chunk_size | Number of rows per chunk (auto-calculated if None) | None | |
target_memory_mb | Target memory per chunk in MB (default 500) | 500 | |
**kwargs | Additional arguments (band_number, band_names, etc.) | {} |
Source code in gigaspatial/processing/tif_processor.py
to_geodataframe(check_memory=True, **kwargs)
¶
Convert the processed TIF data into a GeoDataFrame, where each row represents a pixel zone. Each zone is defined by its bounding box, based on pixel resolution and coordinates.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
check_memory | Whether to check memory before operation | True | |
**kwargs | Additional arguments passed to to_dataframe() | {} |
Returns:
Type | Description |
---|---|
GeoDataFrame | gpd.GeoDataFrame with raster data |
Source code in gigaspatial/processing/tif_processor.py
to_graph(connectivity=4, band=None, include_coordinates=False, graph_type='networkx', check_memory=True)
¶
Convert raster to graph based on pixel adjacency.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
connectivity | Literal[4, 8] | 4 or 8-connectivity | 4 |
band | Optional[int] | Band number (1-indexed) | None |
include_coordinates | bool | Include x,y coordinates in nodes | False |
graph_type | Literal['networkx', 'sparse'] | 'networkx' or 'sparse' | 'networkx' |
check_memory | bool | Whether to check memory before operation | True |
Returns:
Type | Description |
---|---|
Union[Graph, csr_matrix] | Graph representation of raster |
Source code in gigaspatial/processing/tif_processor.py
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add_area_in_meters(gdf, area_column_name='area_in_meters')
¶
Calculate the area of (Multi)Polygon geometries in square meters and add it as a new column.
Parameters:¶
gdf : geopandas.GeoDataFrame GeoDataFrame containing (Multi)Polygon geometries. area_column_name : str, optional Name of the new column to store the area values. Default is "area_m2".
Returns:¶
geopandas.GeoDataFrame The input GeoDataFrame with an additional column for the area in square meters.
Raises:¶
ValueError If the input GeoDataFrame does not contain (Multi)Polygon geometries.
Source code in gigaspatial/processing/geo.py
add_spatial_jitter(df, columns=['latitude', 'longitude'], amount=0.0001, seed=None, copy=True)
¶
Add random jitter to duplicated geographic coordinates to create slight separation between overlapping points.
Parameters:¶
df : pandas.DataFrame DataFrame containing geographic coordinates. columns : list of str, optional Column names containing coordinates to jitter. Default is ['latitude', 'longitude']. amount : float or dict, optional Amount of jitter to add. If float, same amount used for all columns. If dict, specify amount per column, e.g., {'lat': 0.0001, 'lon': 0.0002}. Default is 0.0001 (approximately 11 meters at the equator). seed : int, optional Random seed for reproducibility. Default is None. copy : bool, optional Whether to create a copy of the input DataFrame. Default is True.
Returns:¶
pandas.DataFrame DataFrame with jittered coordinates for previously duplicated points.
Raises:¶
ValueError If columns don't exist or jitter amount is invalid. TypeError If input types are incorrect.
Source code in gigaspatial/processing/geo.py
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aggregate_points_to_zones(points, zones, value_columns=None, aggregation='count', point_zone_predicate='within', zone_id_column='zone_id', output_suffix='', drop_geometry=False)
¶
Aggregate point data to zones with flexible aggregation methods.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
points | Union[DataFrame, GeoDataFrame] | Point data to aggregate | required |
zones | GeoDataFrame | Zones to aggregate points to | required |
value_columns | Optional[Union[str, List[str]]] | Column(s) containing values to aggregate If None, only counts will be performed. | None |
aggregation | Union[str, Dict[str, str]] | Aggregation method(s) to use: - Single string: Use same method for all columns ("count", "mean", "sum", "min", "max") - Dict: Map column names to aggregation methods | 'count' |
point_zone_predicate | str | Spatial predicate for point-to-zone relationship Options: "within", "intersects" | 'within' |
zone_id_column | str | Column in zones containing zone identifiers | 'zone_id' |
output_suffix | str | Suffix to add to output column names | '' |
drop_geometry | bool | Whether to drop the geometry column from output | False |
Returns:
Type | Description |
---|---|
GeoDataFrame | gpd.GeoDataFrame: Zones with aggregated point values |
Example
poi_counts = aggregate_points_to_zones(pois, zones, aggregation="count") poi_value_mean = aggregate_points_to_zones( ... pois, zones, value_columns="score", aggregation="mean" ... ) poi_multiple = aggregate_points_to_zones( ... pois, zones, ... value_columns=["score", "visits"], ... aggregation={"score": "mean", "visits": "sum"} ... )
Source code in gigaspatial/processing/geo.py
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aggregate_polygons_to_zones(polygons, zones, value_columns, aggregation='sum', predicate='intersects', zone_id_column='zone_id', output_suffix='', drop_geometry=False)
¶
Aggregates polygon data to zones based on a specified spatial relationship.
This function performs a spatial join between polygons and zones and then aggregates values from the polygons to their corresponding zones. The aggregation method depends on the predicate
parameter, which determines the nature of the spatial relationship.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
polygons | Union[DataFrame, GeoDataFrame] | Polygon data to aggregate. Must be a GeoDataFrame or convertible to one. | required |
zones | GeoDataFrame | The target zones to which the polygon data will be aggregated. | required |
value_columns | Union[str, List[str]] | The column(s) in | required |
aggregation | Union[str, Dict[str, str]] | The aggregation method(s) to use. Can be a single string (e.g., "sum", "mean", "max") to apply the same method to all columns, or a dictionary mapping column names to aggregation methods (e.g., | 'sum' |
predicate | Literal['intersects', 'within', 'fractional'] | The spatial relationship to use for aggregation: - "intersects": Aggregates values for any polygon that intersects a zone. - "within": Aggregates values for polygons entirely contained within a zone. - "fractional": Performs area-weighted aggregation. The value of a polygon is distributed proportionally to the area of its overlap with each zone. This requires calculating a UTM CRS for accurate area measurements. Defaults to "intersects". | 'intersects' |
zone_id_column | str | The name of the column in | 'zone_id' |
output_suffix | str | A suffix to add to the names of the new aggregated columns in the output GeoDataFrame. Defaults to "". | '' |
drop_geometry | bool | If True, the geometry column will be dropped from the output GeoDataFrame. Defaults to False. | False |
Returns:
Type | Description |
---|---|
GeoDataFrame | gpd.GeoDataFrame: The |
Raises:
Type | Description |
---|---|
TypeError | If |
ValueError | If |
RuntimeError | If an error occurs during the area-weighted aggregation process. |
Example
import geopandas as gpd
Assuming 'landuse_polygons' and 'grid_zones' are GeoDataFrames¶
Aggregate total population within each grid zone using area-weighting¶
pop_by_zone = aggregate_polygons_to_zones( ... landuse_polygons, ... grid_zones, ... value_columns="population", ... predicate="fractional", ... aggregation="sum", ... output_suffix="_pop" ... )
Aggregate the count of landuse parcels intersecting each zone¶
count_by_zone = aggregate_polygons_to_zones( ... landuse_polygons, ... grid_zones, ... value_columns="parcel_id", ... predicate="intersects", ... aggregation="count" ... )
Source code in gigaspatial/processing/geo.py
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annotate_with_admin_regions(gdf, country_code, data_store=None, admin_id_column_suffix='_giga')
¶
Annotate a GeoDataFrame with administrative region information.
Performs a spatial join between the input points and administrative boundaries at levels 1 and 2, resolving conflicts when points intersect multiple admin regions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
gdf | GeoDataFrame | GeoDataFrame containing points to annotate | required |
country_code | str | Country code for administrative boundaries | required |
data_store | Optional[DataStore] | Optional DataStore for loading admin boundary data | None |
Returns:
Type | Description |
---|---|
GeoDataFrame | GeoDataFrame with added administrative region columns |
Source code in gigaspatial/processing/geo.py
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assign_id(df, required_columns, id_column='id')
¶
Generate IDs for any entity type in a pandas DataFrame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
df | DataFrame | Input DataFrame containing entity data | required |
required_columns | List[str] | List of column names required for ID generation | required |
id_column | str | Name for the id column that will be generated | 'id' |
Returns:
Type | Description |
---|---|
DataFrame | pd.DataFrame: DataFrame with generated id column |
Source code in gigaspatial/processing/utils.py
buffer_geodataframe(gdf, buffer_distance_meters, cap_style='round', copy=True)
¶
Buffers a GeoDataFrame with a given buffer distance in meters.
- gdf : geopandas.GeoDataFrame The GeoDataFrame to be buffered.
- buffer_distance_meters : float The buffer distance in meters.
- cap_style : str, optional The style of caps. round, flat, square. Default is round.
- geopandas.GeoDataFrame The buffered GeoDataFrame.
Source code in gigaspatial/processing/geo.py
calculate_pixels_at_location(gdf, resolution, bbox_size=300, crs='EPSG:3857')
¶
Calculates the number of pixels required to cover a given bounding box around a geographic coordinate, given a resolution in meters per pixel.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
gdf | a geodataframe with Point geometries that are geographic coordinates | required | |
resolution | float | Desired resolution (meters per pixel). | required |
bbox_size | float | Bounding box size in meters (default 300m x 300m). | 300 |
crs | str | Target projection (default is EPSG:3857). | 'EPSG:3857' |
Returns:
Name | Type | Description |
---|---|---|
int | Number of pixels per side (width and height). |
Source code in gigaspatial/processing/sat_images.py
convert_to_geodataframe(data, lat_col=None, lon_col=None, crs='EPSG:4326')
¶
Convert a pandas DataFrame to a GeoDataFrame, either from latitude/longitude columns or from a WKT geometry column.
Parameters:¶
data : pandas.DataFrame Input DataFrame containing either lat/lon columns or a geometry column. lat_col : str, optional Name of the latitude column. Default is 'lat'. lon_col : str, optional Name of the longitude column. Default is 'lon'. crs : str or pyproj.CRS, optional Coordinate Reference System of the geometry data. Default is 'EPSG:4326'.
Returns:¶
geopandas.GeoDataFrame A GeoDataFrame containing the input data with a geometry column.
Raises:¶
TypeError If input is not a pandas DataFrame. ValueError If required columns are missing or contain invalid data.
Source code in gigaspatial/processing/geo.py
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detect_coordinate_columns(data, lat_keywords=None, lon_keywords=None, case_sensitive=False)
¶
Detect latitude and longitude columns in a DataFrame using keyword matching.
Parameters:¶
data : pandas.DataFrame DataFrame to search for coordinate columns. lat_keywords : list of str, optional Keywords for identifying latitude columns. If None, uses default keywords. lon_keywords : list of str, optional Keywords for identifying longitude columns. If None, uses default keywords. case_sensitive : bool, optional Whether to perform case-sensitive matching. Default is False.
Returns:¶
tuple[str, str] Names of detected (latitude, longitude) columns.
Raises:¶
ValueError If no unique pair of latitude/longitude columns can be found. TypeError If input data is not a pandas DataFrame.
Source code in gigaspatial/processing/geo.py
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get_centroids(gdf)
¶
Calculate the centroids of a (Multi)Polygon GeoDataFrame.
Parameters:¶
gdf : geopandas.GeoDataFrame GeoDataFrame containing (Multi)Polygon geometries.
Returns:¶
geopandas.GeoDataFrame A new GeoDataFrame with Point geometries representing the centroids.
Raises:¶
ValueError If the input GeoDataFrame does not contain (Multi)Polygon geometries.
Source code in gigaspatial/processing/geo.py
map_points_within_polygons(base_points_gdf, polygon_gdf)
¶
Maps whether each point in base_points_gdf
is within any polygon in polygon_gdf
.
Parameters:¶
base_points_gdf : geopandas.GeoDataFrame GeoDataFrame containing point geometries to check. polygon_gdf : geopandas.GeoDataFrame GeoDataFrame containing polygon geometries.
Returns:¶
geopandas.GeoDataFrame The base_points_gdf
with an additional column is_within
(True/False).
Raises:¶
ValueError If the geometries in either GeoDataFrame are invalid or not of the expected type.
Source code in gigaspatial/processing/geo.py
simplify_geometries(gdf, tolerance=0.01, preserve_topology=True, geometry_column='geometry')
¶
Simplify geometries in a GeoDataFrame to reduce file size and improve visualization performance.
Parameters¶
gdf : geopandas.GeoDataFrame GeoDataFrame containing geometries to simplify. tolerance : float, optional Tolerance for simplification. Larger values simplify more but reduce detail (default is 0.01). preserve_topology : bool, optional Whether to preserve topology while simplifying. Preserving topology prevents invalid geometries (default is True). geometry_column : str, optional Name of the column containing geometries (default is "geometry").
Returns¶
geopandas.GeoDataFrame A new GeoDataFrame with simplified geometries.
Raises¶
ValueError If the specified geometry column does not exist or contains invalid geometries. TypeError If the geometry column does not contain valid geometries.
Examples¶
Simplify geometries in a GeoDataFrame:
simplified_gdf = simplify_geometries(gdf, tolerance=0.05)
Source code in gigaspatial/processing/geo.py
algorithms
¶
build_distance_graph(left_df, right_df, distance_threshold, max_k=100, return_dataframe=False, verbose=True, exclude_same_index=None)
¶
Build a graph of spatial matches between two dataframes using KD-tree.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
left_df | Union[DataFrame, GeoDataFrame] | Left dataframe to match from | required |
right_df | Union[DataFrame, GeoDataFrame] | Right dataframe to match to | required |
distance_threshold | float | Maximum distance for matching (in meters) | required |
max_k | int | Maximum number of neighbors to consider per point (default: 100) | 100 |
return_dataframe | bool | If True, also return the matches DataFrame | False |
verbose | bool | If True, print statistics about the graph | True |
exclude_same_index | Optional[bool] | If True, exclude self-matches. If None, auto-detect based on df equality | None |
Returns:
Type | Description |
---|---|
Union[Graph, Tuple[Graph, DataFrame]] | NetworkX Graph, or tuple of (Graph, DataFrame) if return_dataframe=True |
Raises:
Type | Description |
---|---|
ValueError | If distance_threshold is negative or max_k is not positive |
Source code in gigaspatial/processing/algorithms.py
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geo
¶
add_area_in_meters(gdf, area_column_name='area_in_meters')
¶
Calculate the area of (Multi)Polygon geometries in square meters and add it as a new column.
Parameters:¶
gdf : geopandas.GeoDataFrame GeoDataFrame containing (Multi)Polygon geometries. area_column_name : str, optional Name of the new column to store the area values. Default is "area_m2".
Returns:¶
geopandas.GeoDataFrame The input GeoDataFrame with an additional column for the area in square meters.
Raises:¶
ValueError If the input GeoDataFrame does not contain (Multi)Polygon geometries.
Source code in gigaspatial/processing/geo.py
add_spatial_jitter(df, columns=['latitude', 'longitude'], amount=0.0001, seed=None, copy=True)
¶
Add random jitter to duplicated geographic coordinates to create slight separation between overlapping points.
Parameters:¶
df : pandas.DataFrame DataFrame containing geographic coordinates. columns : list of str, optional Column names containing coordinates to jitter. Default is ['latitude', 'longitude']. amount : float or dict, optional Amount of jitter to add. If float, same amount used for all columns. If dict, specify amount per column, e.g., {'lat': 0.0001, 'lon': 0.0002}. Default is 0.0001 (approximately 11 meters at the equator). seed : int, optional Random seed for reproducibility. Default is None. copy : bool, optional Whether to create a copy of the input DataFrame. Default is True.
Returns:¶
pandas.DataFrame DataFrame with jittered coordinates for previously duplicated points.
Raises:¶
ValueError If columns don't exist or jitter amount is invalid. TypeError If input types are incorrect.
Source code in gigaspatial/processing/geo.py
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aggregate_points_to_zones(points, zones, value_columns=None, aggregation='count', point_zone_predicate='within', zone_id_column='zone_id', output_suffix='', drop_geometry=False)
¶
Aggregate point data to zones with flexible aggregation methods.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
points | Union[DataFrame, GeoDataFrame] | Point data to aggregate | required |
zones | GeoDataFrame | Zones to aggregate points to | required |
value_columns | Optional[Union[str, List[str]]] | Column(s) containing values to aggregate If None, only counts will be performed. | None |
aggregation | Union[str, Dict[str, str]] | Aggregation method(s) to use: - Single string: Use same method for all columns ("count", "mean", "sum", "min", "max") - Dict: Map column names to aggregation methods | 'count' |
point_zone_predicate | str | Spatial predicate for point-to-zone relationship Options: "within", "intersects" | 'within' |
zone_id_column | str | Column in zones containing zone identifiers | 'zone_id' |
output_suffix | str | Suffix to add to output column names | '' |
drop_geometry | bool | Whether to drop the geometry column from output | False |
Returns:
Type | Description |
---|---|
GeoDataFrame | gpd.GeoDataFrame: Zones with aggregated point values |
Example
poi_counts = aggregate_points_to_zones(pois, zones, aggregation="count") poi_value_mean = aggregate_points_to_zones( ... pois, zones, value_columns="score", aggregation="mean" ... ) poi_multiple = aggregate_points_to_zones( ... pois, zones, ... value_columns=["score", "visits"], ... aggregation={"score": "mean", "visits": "sum"} ... )
Source code in gigaspatial/processing/geo.py
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aggregate_polygons_to_zones(polygons, zones, value_columns, aggregation='sum', predicate='intersects', zone_id_column='zone_id', output_suffix='', drop_geometry=False)
¶
Aggregates polygon data to zones based on a specified spatial relationship.
This function performs a spatial join between polygons and zones and then aggregates values from the polygons to their corresponding zones. The aggregation method depends on the predicate
parameter, which determines the nature of the spatial relationship.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
polygons | Union[DataFrame, GeoDataFrame] | Polygon data to aggregate. Must be a GeoDataFrame or convertible to one. | required |
zones | GeoDataFrame | The target zones to which the polygon data will be aggregated. | required |
value_columns | Union[str, List[str]] | The column(s) in | required |
aggregation | Union[str, Dict[str, str]] | The aggregation method(s) to use. Can be a single string (e.g., "sum", "mean", "max") to apply the same method to all columns, or a dictionary mapping column names to aggregation methods (e.g., | 'sum' |
predicate | Literal['intersects', 'within', 'fractional'] | The spatial relationship to use for aggregation: - "intersects": Aggregates values for any polygon that intersects a zone. - "within": Aggregates values for polygons entirely contained within a zone. - "fractional": Performs area-weighted aggregation. The value of a polygon is distributed proportionally to the area of its overlap with each zone. This requires calculating a UTM CRS for accurate area measurements. Defaults to "intersects". | 'intersects' |
zone_id_column | str | The name of the column in | 'zone_id' |
output_suffix | str | A suffix to add to the names of the new aggregated columns in the output GeoDataFrame. Defaults to "". | '' |
drop_geometry | bool | If True, the geometry column will be dropped from the output GeoDataFrame. Defaults to False. | False |
Returns:
Type | Description |
---|---|
GeoDataFrame | gpd.GeoDataFrame: The |
Raises:
Type | Description |
---|---|
TypeError | If |
ValueError | If |
RuntimeError | If an error occurs during the area-weighted aggregation process. |
Example
import geopandas as gpd
Assuming 'landuse_polygons' and 'grid_zones' are GeoDataFrames¶
Aggregate total population within each grid zone using area-weighting¶
pop_by_zone = aggregate_polygons_to_zones( ... landuse_polygons, ... grid_zones, ... value_columns="population", ... predicate="fractional", ... aggregation="sum", ... output_suffix="_pop" ... )
Aggregate the count of landuse parcels intersecting each zone¶
count_by_zone = aggregate_polygons_to_zones( ... landuse_polygons, ... grid_zones, ... value_columns="parcel_id", ... predicate="intersects", ... aggregation="count" ... )
Source code in gigaspatial/processing/geo.py
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annotate_with_admin_regions(gdf, country_code, data_store=None, admin_id_column_suffix='_giga')
¶
Annotate a GeoDataFrame with administrative region information.
Performs a spatial join between the input points and administrative boundaries at levels 1 and 2, resolving conflicts when points intersect multiple admin regions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
gdf | GeoDataFrame | GeoDataFrame containing points to annotate | required |
country_code | str | Country code for administrative boundaries | required |
data_store | Optional[DataStore] | Optional DataStore for loading admin boundary data | None |
Returns:
Type | Description |
---|---|
GeoDataFrame | GeoDataFrame with added administrative region columns |
Source code in gigaspatial/processing/geo.py
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buffer_geodataframe(gdf, buffer_distance_meters, cap_style='round', copy=True)
¶
Buffers a GeoDataFrame with a given buffer distance in meters.
- gdf : geopandas.GeoDataFrame The GeoDataFrame to be buffered.
- buffer_distance_meters : float The buffer distance in meters.
- cap_style : str, optional The style of caps. round, flat, square. Default is round.
- geopandas.GeoDataFrame The buffered GeoDataFrame.
Source code in gigaspatial/processing/geo.py
convert_to_geodataframe(data, lat_col=None, lon_col=None, crs='EPSG:4326')
¶
Convert a pandas DataFrame to a GeoDataFrame, either from latitude/longitude columns or from a WKT geometry column.
Parameters:¶
data : pandas.DataFrame Input DataFrame containing either lat/lon columns or a geometry column. lat_col : str, optional Name of the latitude column. Default is 'lat'. lon_col : str, optional Name of the longitude column. Default is 'lon'. crs : str or pyproj.CRS, optional Coordinate Reference System of the geometry data. Default is 'EPSG:4326'.
Returns:¶
geopandas.GeoDataFrame A GeoDataFrame containing the input data with a geometry column.
Raises:¶
TypeError If input is not a pandas DataFrame. ValueError If required columns are missing or contain invalid data.
Source code in gigaspatial/processing/geo.py
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detect_coordinate_columns(data, lat_keywords=None, lon_keywords=None, case_sensitive=False)
¶
Detect latitude and longitude columns in a DataFrame using keyword matching.
Parameters:¶
data : pandas.DataFrame DataFrame to search for coordinate columns. lat_keywords : list of str, optional Keywords for identifying latitude columns. If None, uses default keywords. lon_keywords : list of str, optional Keywords for identifying longitude columns. If None, uses default keywords. case_sensitive : bool, optional Whether to perform case-sensitive matching. Default is False.
Returns:¶
tuple[str, str] Names of detected (latitude, longitude) columns.
Raises:¶
ValueError If no unique pair of latitude/longitude columns can be found. TypeError If input data is not a pandas DataFrame.
Source code in gigaspatial/processing/geo.py
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get_centroids(gdf)
¶
Calculate the centroids of a (Multi)Polygon GeoDataFrame.
Parameters:¶
gdf : geopandas.GeoDataFrame GeoDataFrame containing (Multi)Polygon geometries.
Returns:¶
geopandas.GeoDataFrame A new GeoDataFrame with Point geometries representing the centroids.
Raises:¶
ValueError If the input GeoDataFrame does not contain (Multi)Polygon geometries.
Source code in gigaspatial/processing/geo.py
map_points_within_polygons(base_points_gdf, polygon_gdf)
¶
Maps whether each point in base_points_gdf
is within any polygon in polygon_gdf
.
Parameters:¶
base_points_gdf : geopandas.GeoDataFrame GeoDataFrame containing point geometries to check. polygon_gdf : geopandas.GeoDataFrame GeoDataFrame containing polygon geometries.
Returns:¶
geopandas.GeoDataFrame The base_points_gdf
with an additional column is_within
(True/False).
Raises:¶
ValueError If the geometries in either GeoDataFrame are invalid or not of the expected type.
Source code in gigaspatial/processing/geo.py
simplify_geometries(gdf, tolerance=0.01, preserve_topology=True, geometry_column='geometry')
¶
Simplify geometries in a GeoDataFrame to reduce file size and improve visualization performance.
Parameters¶
gdf : geopandas.GeoDataFrame GeoDataFrame containing geometries to simplify. tolerance : float, optional Tolerance for simplification. Larger values simplify more but reduce detail (default is 0.01). preserve_topology : bool, optional Whether to preserve topology while simplifying. Preserving topology prevents invalid geometries (default is True). geometry_column : str, optional Name of the column containing geometries (default is "geometry").
Returns¶
geopandas.GeoDataFrame A new GeoDataFrame with simplified geometries.
Raises¶
ValueError If the specified geometry column does not exist or contains invalid geometries. TypeError If the geometry column does not contain valid geometries.
Examples¶
Simplify geometries in a GeoDataFrame:
simplified_gdf = simplify_geometries(gdf, tolerance=0.05)
Source code in gigaspatial/processing/geo.py
sat_images
¶
calculate_pixels_at_location(gdf, resolution, bbox_size=300, crs='EPSG:3857')
¶
Calculates the number of pixels required to cover a given bounding box around a geographic coordinate, given a resolution in meters per pixel.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
gdf | a geodataframe with Point geometries that are geographic coordinates | required | |
resolution | float | Desired resolution (meters per pixel). | required |
bbox_size | float | Bounding box size in meters (default 300m x 300m). | 300 |
crs | str | Target projection (default is EPSG:3857). | 'EPSG:3857' |
Returns:
Name | Type | Description |
---|---|---|
int | Number of pixels per side (width and height). |
Source code in gigaspatial/processing/sat_images.py
tif_processor
¶
TifProcessor
¶
A class to handle tif data processing, supporting single-band, RGB, RGBA, and multi-band data. Can merge multiple rasters into one during initialization.
Source code in gigaspatial/processing/tif_processor.py
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|
bounds
property
¶
Get the bounds of the TIF file
count: int
property
¶
Get the band count from the TIF file
crs
property
¶
Get the coordinate reference system from the TIF file
dtype
property
¶
Get the data types from the TIF file
is_merged: bool
property
¶
Check if this processor was created from multiple rasters.
nodata: int
property
¶
Get the value representing no data in the rasters
resolution: Tuple[float, float]
property
¶
Get the x and y resolution (pixel width and height or pixel size) from the TIF file
source_count: int
property
¶
Get the number of source rasters.
transform
property
¶
Get the transform from the TIF file
x_transform: float
property
¶
Get the x transform from the TIF file
y_transform: float
property
¶
Get the y transform from the TIF file
__del__()
¶
__exit__(exc_type, exc_value, traceback)
¶
__post_init__()
¶
Validate inputs, merge rasters if needed, and set up logging.
Source code in gigaspatial/processing/tif_processor.py
cleanup()
¶
Explicit cleanup method for better control.
clip_to_bounds(bounds, bounds_crs=None, return_clipped_processor=True)
¶
Clip raster to rectangular bounds.
Parameters:¶
bounds : tuple Bounding box as (minx, miny, maxx, maxy) bounds_crs : str, optional CRS of the bounds. If None, assumes same as raster CRS return_clipped_processor : bool, default True If True, returns new TifProcessor, else returns (array, transform, metadata)
Returns:¶
TifProcessor or tuple Either new TifProcessor instance or (array, transform, metadata) tuple
Source code in gigaspatial/processing/tif_processor.py
clip_to_geometry(geometry, crop=True, all_touched=True, invert=False, nodata=None, pad=False, pad_width=0.5, return_clipped_processor=True)
¶
Clip raster to geometry boundaries.
Parameters:¶
geometry : various Geometry to clip to. Can be: - Shapely Polygon or MultiPolygon - GeoDataFrame or GeoSeries - List of GeoJSON-like dicts - Single GeoJSON-like dict crop : bool, default True Whether to crop the raster to the extent of the geometry all_touched : bool, default True Include pixels that touch the geometry boundary invert : bool, default False If True, mask pixels inside geometry instead of outside nodata : int or float, optional Value to use for masked pixels. If None, uses raster's nodata value pad : bool, default False Pad geometry by half pixel before clipping pad_width : float, default 0.5 Width of padding in pixels if pad=True return_clipped_processor : bool, default True If True, returns new TifProcessor with clipped data If False, returns (clipped_array, transform, metadata)
Returns:¶
TifProcessor or tuple Either new TifProcessor instance or (array, transform, metadata) tuple
Source code in gigaspatial/processing/tif_processor.py
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|
get_raster_info()
¶
Get comprehensive raster information.
Source code in gigaspatial/processing/tif_processor.py
open_dataset()
¶
Context manager for accessing the dataset, handling temporary reprojected files.
Source code in gigaspatial/processing/tif_processor.py
reproject_to(target_crs, output_path=None, resampling_method=None, resolution=None)
¶
Reprojects the current raster to a new CRS and optionally saves it.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
target_crs | str | The CRS to reproject to (e.g., "EPSG:4326"). | required |
output_path | Optional[Union[str, Path]] | The path to save the reprojected raster. If None, it is saved to a temporary file. | None |
resampling_method | Optional[Resampling] | The resampling method to use. | None |
resolution | Optional[Tuple[float, float]] | The target resolution (pixel size) in the new CRS. | None |
Source code in gigaspatial/processing/tif_processor.py
sample_by_polygons(polygon_list, stat='mean')
¶
Sample raster values by polygons and compute statistic(s) for each polygon.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
polygon_list | List of shapely Polygon or MultiPolygon objects. | required | |
stat | Union[str, Callable, List[Union[str, Callable]]] | Statistic(s) to compute. Can be: - Single string: 'mean', 'median', 'sum', 'min', 'max', 'std', 'count' - Single callable: custom function that takes array and returns scalar - List of strings/callables: multiple statistics to compute | 'mean' |
Returns:
Type | Description |
---|---|
If single stat: np.ndarray of computed statistics for each polygon | |
If multiple stats: List of dictionaries with stat names as keys |
Source code in gigaspatial/processing/tif_processor.py
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|
sample_by_polygons_batched(polygon_list, stat='mean', batch_size=100, n_workers=4, show_progress=True, check_memory=True, **kwargs)
¶
Sample raster values by polygons in parallel using batching.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
polygon_list | List[Union[Polygon, MultiPolygon]] | List of Shapely Polygon or MultiPolygon objects | required |
stat | Union[str, Callable] | Statistic to compute | 'mean' |
batch_size | int | Number of polygons per batch | 100 |
n_workers | int | Number of worker processes | 4 |
show_progress | bool | Whether to display progress bar | True |
check_memory | bool | Whether to check memory before operation | True |
**kwargs | Additional arguments | {} |
Returns:
Type | Description |
---|---|
ndarray | np.ndarray of statistics for each polygon |
Source code in gigaspatial/processing/tif_processor.py
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to_dataframe(drop_nodata=True, check_memory=True, **kwargs)
¶
Convert raster to DataFrame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
drop_nodata | Whether to drop nodata values | True | |
check_memory | Whether to check memory before operation (default True) | True | |
**kwargs | Additional arguments | {} |
Returns:
Type | Description |
---|---|
DataFrame | pd.DataFrame with raster data |
Source code in gigaspatial/processing/tif_processor.py
to_dataframe_chunked(drop_nodata=True, chunk_size=None, target_memory_mb=500, **kwargs)
¶
Convert raster to DataFrame using chunked processing for memory efficiency.
Automatically routes to the appropriate chunked method based on mode. Chunk size is automatically calculated based on target memory usage.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
drop_nodata | Whether to drop nodata values | True | |
chunk_size | Number of rows per chunk (auto-calculated if None) | None | |
target_memory_mb | Target memory per chunk in MB (default 500) | 500 | |
**kwargs | Additional arguments (band_number, band_names, etc.) | {} |
Source code in gigaspatial/processing/tif_processor.py
to_geodataframe(check_memory=True, **kwargs)
¶
Convert the processed TIF data into a GeoDataFrame, where each row represents a pixel zone. Each zone is defined by its bounding box, based on pixel resolution and coordinates.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
check_memory | Whether to check memory before operation | True | |
**kwargs | Additional arguments passed to to_dataframe() | {} |
Returns:
Type | Description |
---|---|
GeoDataFrame | gpd.GeoDataFrame with raster data |
Source code in gigaspatial/processing/tif_processor.py
to_graph(connectivity=4, band=None, include_coordinates=False, graph_type='networkx', check_memory=True)
¶
Convert raster to graph based on pixel adjacency.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
connectivity | Literal[4, 8] | 4 or 8-connectivity | 4 |
band | Optional[int] | Band number (1-indexed) | None |
include_coordinates | bool | Include x,y coordinates in nodes | False |
graph_type | Literal['networkx', 'sparse'] | 'networkx' or 'sparse' | 'networkx' |
check_memory | bool | Whether to check memory before operation | True |
Returns:
Type | Description |
---|---|
Union[Graph, csr_matrix] | Graph representation of raster |
Source code in gigaspatial/processing/tif_processor.py
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|
utils
¶
assign_id(df, required_columns, id_column='id')
¶
Generate IDs for any entity type in a pandas DataFrame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
df | DataFrame | Input DataFrame containing entity data | required |
required_columns | List[str] | List of column names required for ID generation | required |
id_column | str | Name for the id column that will be generated | 'id' |
Returns:
Type | Description |
---|---|
DataFrame | pd.DataFrame: DataFrame with generated id column |