# import json
import pandas as pd
[docs]
def make_gage_data_csv(file_path: str):
"returns df"
with open(file_path) as f:
df = pd.read_json(f)
df = df.T
df.index.name = "id"
return df
# todo define this function properly (what is econet?)
# def make_station_meta(file_path_eco: str, file_path_assos: str):
# core_columns = econet[['Station', 'Name', 'Latitude', 'Longitude',
# 'Elevation', 'First Ob', 'Supported By', 'Time Interval(s)', 'Precip']]
# todo define this function properly (haversine is not defined)
# def get_closest_gage_list(station_df: pd.DataFrame, gage_df: pd.DataFrame):
# for row in gage_df.iterrows():
# gage_info = {}
# gage_info["river_id"] = row[1]['id']
# gage_lat = row[1]['latitude']
# gage_long = row[1]['logitude']
# gage_info["stations"] = []
# for stat_row in station_df.iterrows():
# dist = haversine(stat_row[1]["lon"], stat_row[1]["lat"], gage_long, gage_lat)
# st_id = stat_row[1]['stid']
# gage_info["stations"].append({"station_id": st_id, "dist": dist})
# gage_info["stations"] = sorted(gage_info['stations'], key=lambda i: i["dist"], reverse=True)
# print(gage_info)
# with open(str(gage_info["river_id"]) + "stations.json", 'w') as w:
# json.dump(gage_info, w)