# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""Dataset for EDDMapS."""
import functools
import os
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
import rasterio
import torch
from geopandas import GeoDataFrame
from matplotlib.figure import Figure
from .errors import DatasetNotFoundError
from .geo import GeoDataset
from .utils import GeoSlice, Path, Sample, disambiguate_timestamp
[docs]
class EDDMapS(GeoDataset):
"""Dataset for EDDMapS.
`EDDMapS <https://www.eddmaps.org/>`__, Early Detection and Distribution Mapping
System, is a web-based mapping system for documenting invasive species and pest
distribution. Launched in 2005 by the Center for Invasive Species and Ecosystem
Health at the University of Georgia, it was originally designed as a tool for
state Exotic Pest Plant Councils to develop more complete distribution data of
invasive species. Since then, the program has expanded to include the entire US
and Canada as well as to document certain native pest species.
EDDMapS query results can be downloaded in CSV, KML, or Shapefile format. This
dataset currently only supports CSV files.
If you use an EDDMapS dataset in your research, please cite it like so:
* EDDMapS. *YEAR*. Early Detection & Distribution Mapping System. The University of
Georgia - Center for Invasive Species and Ecosystem Health. Available online at
https://www.eddmaps.org/; last accessed *DATE*.
.. versionadded:: 0.3
"""
[docs]
def __init__(self, root: Path = 'data') -> None:
"""Initialize a new Dataset instance.
Args:
root: root directory where dataset can be found
Raises:
DatasetNotFoundError: If dataset is not found.
"""
super().__init__()
self.root = root
filepath = os.path.join(root, 'mappings.csv')
if not os.path.exists(filepath):
raise DatasetNotFoundError(self)
# Read CSV file
df = pd.read_csv(filepath, usecols=['ObsDate', 'Latitude', 'Longitude'])
df = df[df.Latitude.notna()]
df = df[df.Longitude.notna()]
# Convert from pandas DataFrame to geopandas GeoDataFrame
func = functools.partial(disambiguate_timestamp, format='%m-%d-%y')
data = df['ObsDate'].apply(func).to_list()
index = pd.IntervalIndex.from_tuples(data, closed='both', name='datetime')
geometry = gpd.points_from_xy(df.Longitude, df.Latitude)
self.index = GeoDataFrame(index=index, geometry=geometry, crs='EPSG:4326')
[docs]
def __getitem__(self, index: GeoSlice) -> Sample:
"""Retrieve input, target, and/or metadata indexed by spatiotemporal slice.
Args:
index: [xmin:xmax:xres, ymin:ymax:yres, tmin:tmax:tres] coordinates to index.
Returns:
Sample of input, target, and/or metadata at that index.
Raises:
IndexError: If *index* is not found in the dataset.
"""
x, y, t = self._disambiguate_slice(index)
interval = pd.Interval(t.start, t.stop)
df = self.index.iloc[self.index.index.overlaps(interval)]
df = df.iloc[:: t.step]
df = df.cx[x.start : x.stop, y.start : y.stop]
if df.empty:
raise IndexError(
f'index: {index} not found in dataset with bounds: {self.bounds}'
)
keypoints = torch.tensor(df.get_coordinates().values, dtype=torch.float32)
transform = rasterio.transform.from_origin(x.start, y.stop, x.step, y.step)
sample = {
'bounds': self._slice_to_tensor(index),
'keypoints': keypoints,
'transform': torch.tensor(transform),
}
return sample
[docs]
def plot(
self, sample: Sample, show_titles: bool = True, suptitle: str | None = None
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample return by :meth:`__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional suptitle to use for Figure
Returns:
a matplotlib Figure with the rendered sample
.. versionadded:: 0.8
"""
# Create figure and axis - using regular matplotlib axes
fig, ax = plt.subplots(figsize=(10, 8))
ax.grid(ls='--')
# Extract coordinates
keypoints = sample['keypoints']
x = keypoints[:, 0]
y = keypoints[:, 1]
# Plot the points
ax.scatter(x, y)
# Set labels
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
# Add titles if requested
if show_titles:
ax.set_title('EDDMapS Observation Locations by Date')
if suptitle is not None:
fig.suptitle(suptitle)
fig.tight_layout()
return fig
