Hans Kristian Djuve
83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
import requests
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import pandas as pd
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import numpy as np
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from pyproj import Transformer
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# create transformer once (faster if used repeatedly)
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_transformer = Transformer.from_crs("EPSG:4326", "EPSG:3857", always_xy=True)
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def main(lat, lon, radius_m,
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n_vertices=15,
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page_size=10000,
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species_summary=False):
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"""
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Fetch Artsdatabanken observations inside a semicircular polygon.
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Parameters
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----------
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lat : float
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lon : float
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radius_m : float
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Radius in meters
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n_vertices : int
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Polygon vertices approximating the circle
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page_size : int
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species_summary : bool
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If True, also return species counts
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Returns
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-------
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pandas.DataFrame
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(optional) pandas.Series with species counts
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"""
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# -------------------
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# Build polygon
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# -------------------
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cx, cy = _transformer.transform(lon, lat)
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angles = np.linspace(0, 2*np.pi, n_vertices, endpoint=False)
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x = np.float32(cx + radius_m * np.cos(angles))
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y = np.float32(cy + radius_m * np.sin(angles))
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points = [f"{xi}+{yi}" for xi, yi in zip(x, y)]
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points.append(points[0]) # close polygon
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polygon = "POLYGON((" + ",".join(points) + "))"
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# -------------------
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# API request
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# -------------------
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url = (
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"https://artskart.artsdatabanken.no/publicapi/api/observations/list/"
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f"?gmWktPolygon={polygon}&page=0&pageSize={page_size}"
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)
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r = requests.get(url)
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r.raise_for_status()
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data = r.json()
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if "Observations" not in data:
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return pd.DataFrame()
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# -------------------
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# Dataframe creation
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# -------------------
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df = pd.json_normalize(data["Observations"])[
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["Latitude", "Longitude", "Notes", "ScientificName", "ScientificNameId"]
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]
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df["Latitude"] = df["Latitude"].str.replace(",", ".").astype(float)
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df["Longitude"] = df["Longitude"].str.replace(",", ".").astype(float)
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if species_summary:
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return df, df["ScientificName"].value_counts()
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return df
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