easyclimate_map.core¶

Submodules¶

Classes¶

DataNode

A hierarchical data structure that dynamically manages attributes and nested nodes.

Functions¶

`get_geometry_path`(→ easyclimate_map.core.datanode.DataNode)	Convert geometries from a GeoDataFrame into matplotlib clipping paths.
`clip_rectangle_geometry`(...)	Clip a GeoDataFrame using a rectangular bounding box.
`open_datanode`(→ DataNode)	Load a DataNode object from native location.
`read_shapefile_from_7z`(→ geopandas.GeoDataFrame)	Read a shapefile directly from a 7z archive without extracting to disk.
`extract_outer_boundary`(→ geopandas.GeoDataFrame)	Extract the outer boundary (exterior ring only) from a GeoDataFrame.
`transfer_boundary_to_polygon`(→ geopandas.GeoDataFrame)	Convert boundary lines (LineString or MultiLineString) to polygon geometries.

Package Contents¶

easyclimate_map.core.get_geometry_path(gdf: geopandas.geodataframe.GeoDataFrame, ax: matplotlib.axes.Axes) → easyclimate_map.core.datanode.DataNode¶

Convert geometries from a GeoDataFrame into matplotlib clipping paths.

This function extracts geometries from a GeoDataFrame, projects them to the coordinate system of the given matplotlib axis, and converts them into matplotlib Path objects suitable for clipping or other operations.

Parameters¶

gdfgeopandas.GeoDataFrame: The GeoDataFrame containing geometries to be converted. Geometries should be in geographic coordinates (longitude/latitude in degrees) using the EPSG:4326 (PlateCarree) coordinate reference system.
axmatplotlib.axes.Axes: The matplotlib axis with a cartopy projection. The axis must have a cartopy.crs projection set, which will be used to transform the geometries from geographic coordinates to the map’s coordinate system.

Returns¶

easyclimate-map.DataNode

A DataNode object containing two items:

clip_path: matplotlib.path.Path
A compound Path object containing all projected geometries.
transform_clip_path: matplotlib.transforms.TransformedPath
The same Path object transformed to data coordinates of the axis.

Notes¶

The function assumes input geometries are in geographic coordinates (EPSG:4326 / PlateCarree).
The axis must use a cartopy projection. Standard matplotlib axes without cartopy will not work.
The resulting Path objects can be used for clipping matplotlib plots or other operations that require vector paths.

Examples¶

>>> import matplotlib.pyplot as plt
>>> import cartopy.crs as ccrs
>>> import geopandas as gpd
>>>
>>> # Create a map with cartopy projection
>>> fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})
>>> # Load GeoDataFrame (assumed to be in geographic coordinates)
>>> gdf = gpd.read_file('path/to/shapefile.shp')
>>> # Convert geometries to clipping paths
>>> paths = get_geometry_path(gdf, ax)
>>> # Use the paths for clipping
>>> im = ax.imshow(data, transform=ccrs.PlateCarree())
>>> im.set_clip_path(paths['transform_clip_path'])

Parameters¶

gdfgeopandas.GeoDataFrame: The GeoDataFrame to clip
*argsUnion[float, List[float], Tuple[float]]: Either four separate float arguments (lon1, lon2, lat1, lat2) or a single list/tuple containing four floats

Returns¶

geopandas.GeoDataFrame: The clipped GeoDataFrame

Examples¶

>>> # Method 1: Four separate arguments
>>> clipped = clip_rectangle_geometry(gdf, 110, 130, 30, 40)
>>>
>>> # Method 2: Single list argument
>>> clipped = clip_rectangle_geometry(gdf, [110, 130, 30, 40])

class easyclimate_map.core.DataNode(name='root')¶

A hierarchical data structure that dynamically manages attributes and nested nodes.

The DataNode class provides a flexible way to organize and access data in a tree-like structure. It supports automatic creation of nested nodes, path-style access, and rich HTML representation in Jupyter environments.

Parameters¶

namestr, optional: The name of the root node (default: "root").

_attributes¶

name = 'root'¶

__getattr__(key)¶

Dynamically access or create node attributes.

Automatically creates nested DataNode for non-existent attributes, while filtering out special IPython/Jupyter attribute requests.

Parameters¶

keystr: The attribute name to access.

Returns¶

Any: The requested attribute or a new DataNode if attribute doesn’t exist.

Raises¶

AttributeError: If the attribute is a special IPython/Jupyter attribute.

__setattr__(key, value)¶

Set node attributes while protecting internal attributes.

Parameters¶

keystr: The attribute name to set.
valueAny: The value to assign to the attribute.

__getitem__(key)¶

Access attributes using path-style notation (e.g., “path/to/attribute”).

Parameters¶

keystr: The attribute path to access, with ‘/’ separators for nested nodes.

Returns¶

Any: The value at the specified path.

Raises¶

KeyError: If any part of the path doesn’t exist.

__contains__(key)¶

__setitem__(key, value)¶

Set attributes using path-style notation, creating intermediate nodes as needed.

Parameters¶

keystr: The attribute path to set, with ‘/’ separators for nested nodes.
valueAny: The value to assign at the specified path.

_repr_html_()¶

Generate HTML representation for Jupyter notebooks.

Returns¶

str: HTML string representing the node and its contents.

_format_html()¶

Generate complete HTML representation including styles and scripts.

Returns¶

str: Complete HTML document as a string.

_format_node_html(node, level=0, parent_id=None)¶

Recursively generate HTML for a node and its children.

Parameters¶

nodeDataNode: The node to format.
levelint, optional: Current nesting level (default: 0).
parent_idstr, optional: ID of parent node for DOM construction (default: None).

Returns¶

str: HTML string representing the node.

_format_value(value)¶

Format values for display, truncating long sequences.

Parameters¶

valueAny: The value to format.

Returns¶

str: Formatted string representation of the value.

format_tree(level=0, html=False, is_last_list=None)¶

Generate a tree-structured representation of the node.

Parameters¶

levelint, optional: Current indentation level (default: 0).
htmlbool, optional: Whether to generate HTML output (default: False).
is_last_listlist of bool, optional: Track position in hierarchy for proper indentation (default: None).

Returns¶

str: Formatted tree representation.

__repr__()¶

_repr_pretty_(p, cycle)¶: Support the IPython of pretty printing

_repr_mimebundle_(include=None, exclude=None)¶

__dir__()¶

to_zarr(filepath: str | pathlib.Path, zarr_format: Literal[2, 3] = 2)¶

Save the DataNode and its contents to a Zarr storage format.

Parameters¶

filepathUnion[str, Path]: Directory path to save the data.
zarr_formatLiteral[2, 3], optional: Zarr storage format version (default: 2).

classmethod load(filepath: str | pathlib.Path)¶

Load a DataNode from a Zarr storage directory.

Parameters¶

filepathUnion[str, Path]: Directory path containing the saved DataNode.

Returns¶

DataNode: The reconstructed DataNode with all its attributes.

easyclimate_map.core.open_datanode(filepath: str) → DataNode¶

Load a DataNode object from native location.

This function provides a convenient way to load a DataNode that was previously saved using the DataNode.to_zarr() method.

Parameters¶

filepathstr: The path to the directory containing the saved DataNode data. This should be the same path used with DataNode.to_zarr().

Returns¶

DataNode: The loaded DataNode object with all its attributes and nested structure.

Examples¶

>>> node = open_datanode("path/to/saved_node")
>>> node.some_attribute  # Access attributes as usual

easyclimate_map.core.read_shapefile_from_7z(filepath: str, **kwargs) → geopandas.GeoDataFrame¶

Read a shapefile directly from a 7z archive without extracting to disk.

This function extracts a shapefile from a compressed 7z archive into a temporary directory, loads it as a GeoDataFrame using GeoPandas, and returns the result. All keyword arguments are passed through to gpd.read_file().

Parameters¶

filepathstr

Path to the 7z archive containing the shapefile.

**kwargsdict, optional

Additional keyword arguments to pass to gpd.read_file(). Common arguments include: - bbox : tuple

Filter by bounding box (minx, miny, maxx, maxy)

maskgeometry
Filter by geometry mask
rowsint
Number of rows to read
encodingstr
File encoding (e.g., ‘utf-8’)
driverstr
GDAL/OGR driver name

Returns¶

geopandas.GeoDataFrame: A GeoDataFrame containing the shapefile data.

Raises¶

FileNotFoundError: If the 7z file doesn’t exist or doesn’t contain any .shp files.

Notes¶

The function creates a temporary directory that is automatically cleaned up.
Only the first .shp file found in the archive will be read.
Shapefile companion files (.shx, .dbf, .prj) must also be present in the archive.

Examples¶

>>> gdf = read_shapefile_from_7z('data.7z')
>>> gdf = read_shapefile_from_7z('data.7z', bbox=(xmin, ymin, xmax, ymax))
>>> gdf = read_shapefile_from_7z('data.7z', encoding='utf-8', rows=1000)

Parameters¶

gdfgeopandas.GeoDataFrame: Input GeoDataFrame containing polygon or multipolygon geometries.
dissolve_bystr or list of str, optional: Column name(s) to dissolve by. If None (default), dissolves all features into a single geometry.

Returns¶

geopandas.GeoDataFrame: A GeoDataFrame containing only the outer boundary lines (LineString or MultiLineString geometry).

Examples¶

>>> import geopandas as gpd
>>> # Load your data
>>> gdf = gpd.read_file('basins.shp')
>>>
>>> # Extract outer boundary of all features
>>> boundary = extract_outer_boundary(gdf)
>>>
>>> # Extract boundary by specific attribute
>>> boundary = extract_outer_boundary(gdf, dissolve_by='BasinName')
>>>
>>> # Save the result
>>> boundary.to_file('boundary.shp')

Notes¶

For MULTIPOLYGON geometries, extracts the exterior ring of each polygon part
Interior holes (e.g., lakes, enclaves) are excluded
The output CRS is preserved from the input GeoDataFrame
If input contains multiple disconnected regions, output will be MultiLineString

Parameters¶

boundary_gdfgeopandas.GeoDataFrame: Input GeoDataFrame containing LineString or MultiLineString geometries representing polygon boundaries.

Returns¶

geopandas.GeoDataFrame: A GeoDataFrame containing reconstructed Polygon or MultiPolygon geometries.

Examples¶

>>> import geopandas as gpd
>>> # Extract boundary first
>>> boundary = extract_outer_boundary(gdf)
>>>
>>> # Convert boundary back to polygon
>>> polygon = boundary_to_polygon(boundary)
>>>
>>> # Save the result
>>> polygon.to_file('reconstructed_polygon.shp')

Notes¶

Input boundary lines must form closed rings
For MultiLineString input, creates MultiPolygon output
The output CRS is preserved from the input GeoDataFrame
This creates simple polygons without interior holes

easyclimate_map.core¶

Submodules¶

Classes¶

Functions¶

Package Contents¶

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Returns¶

Notes¶

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See Also¶

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