Hans Kristian Djuve
150 lines
4.5 KiB
Python
150 lines
4.5 KiB
Python
import requests
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import pandas as pd
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import numpy as np
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def main(loc_id):
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"""
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Fetch survey history for a fish farm location ID and expand with measurement data
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for report_type 'b' (b-undersøkelser).
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Adds columns:
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- lat (list)
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- lon (list)
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- b_float_value (list)
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- tilstand_final (list)
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- depth (list)
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- measurement number (list)
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For reports without measurement data (including type 'c'),
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these columns contain empty lists.
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"""
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loc_id = str(loc_id)
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base_url = "https://api.fiskeridir.no/envreportreg-public/api/v2/report/search"
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params = {"siteNumber": loc_id}
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response = requests.get(base_url, params=params)
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response.raise_for_status()
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data = response.json()
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items = data.get("content", [])
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if not items:
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return pd.DataFrame(columns=[
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"report_type", "report_date", "report_id", "download_link",
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"lat", "lon", "b_float_value", "tilstand_final"
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])
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dl_link = "https://api.fiskeridir.no/envreportreg-public/api/v2/report/"
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# Build base dataframe
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df = pd.DataFrame({
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"report_type": [item.get("envExaminationType") for item in items],
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"report_date": [item.get("reportCreated") for item in items],
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"report_id": [int(item.get("reportId")) for item in items],
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"download_link" : [dl_link + str(item.get("reportId")) + '/pdf' for item in items],
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})
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# Convert and sort
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df["report_date"] = pd.to_datetime(df["report_date"], utc=True)
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df = df.sort_values(
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by=["report_type", "report_date"],
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ascending=[True, False]
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).reset_index(drop=True)
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# Prepare empty columns (lists per row)
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df["lat"] = [[] for _ in range(len(df))]
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df["lon"] = [[] for _ in range(len(df))]
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df["b_float_value"] = [[] for _ in range(len(df))]
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df["tilstand_final"] = [[] for _ in range(len(df))]
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df["depth"] = [[] for _ in range(len(df))]
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df["measurement_nr"] = [[] for _ in range(len(df))]
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# Process only report_type 'b'
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for idx, row in df.iterrows():
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if row["report_type"] != "b":
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continue
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report_id = str(row["report_id"])
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detail_url = f"https://api.fiskeridir.no/envreportreg-public/api/v2/report/{report_id}"
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response = requests.get(detail_url)
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response.raise_for_status()
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detail_data = response.json()
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measurements = detail_data.get("measurements", [])
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if not measurements:
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continue # leave empty lists
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# Extract values
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lat = np.array([m.get("latitudeDecimal") for m in measurements])
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lon = np.array([m.get("longitudeDecimal") for m in measurements])
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b_float_value = np.array([m.get("medianGroupIIAndIIIValue") for m in measurements])
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depth = np.array([m.get("depthMeters") for m in measurements])
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measurement_nr = np.array([m.get("stationMeasurementNumber") for m in measurements])
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# Keep only valid coordinates
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valid_idx = np.where(lat > 0)[0]
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lat = lat[valid_idx]
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lon = lon[valid_idx]
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b_float_value = b_float_value[valid_idx]
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depth = depth[valid_idx]
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measurement_nr = measurement_nr[valid_idx]
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# Compute tilstand_final (vectorized)
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tilstand_final = np.select(
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[
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b_float_value < 1.1,
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(b_float_value >= 1.1) & (b_float_value < 2.1),
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(b_float_value >= 2.1) & (b_float_value < 3.1),
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],
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[1, 2, 3],
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default=4
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)
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# Store as lists in dataframe
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df.at[idx, "lat"] = lat.tolist()
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df.at[idx, "lon"] = lon.tolist()
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df.at[idx, "b_float_value"] = b_float_value.tolist()
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df.at[idx, "tilstand_final"] = tilstand_final.tolist()
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df.at[idx, "depth"] = depth.tolist()
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df.at[idx, "measurement_nr"] = measurement_nr.tolist()
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# Convert date back to string if desired
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df["report_date"] = df["report_date"].dt.strftime("%Y-%m-%dT%H:%M:%SZ")
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'''Example for plotting b_survey results:
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import matplotlib.pyplot as plt
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survey = 2
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color_map = {
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1: 'blue',
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2: 'green',
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3: 'yellow',
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4: 'red'
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}
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#colors = pd['tilstand_final'][0].map(color_map)
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colors = pd.Series(df2['tilstand_final'][survey]).map(color_map).tolist()
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plt.figure()
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plt.scatter(
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df2['lon'][survey],
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df2['lat'][survey],
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c=colors,
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marker='s' # square
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)
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plt.xlabel('Longitude')
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plt.ylabel('Latitude')
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plt.title('Tilstand Plot B rapport '+ df2['report_date'][survey])
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plt.show()'''
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return df |