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import streamlit as st |
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import pandas as pd |
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import geopandas as gpd |
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import requests |
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import json |
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import time |
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class DataModel: |
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def __init__(self,): |
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self.base_url = "https://infrasense-dashboard-aggregation-service-prod.delta.k8s-wdy.de" |
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self.crs_dist = 32630 |
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self.crs_ll = 4326 |
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self.sleep = 5 |
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@st.cache_data(show_spinner='Daten werden geladen ...') |
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def get_data(_self, osm_id, radius=25): |
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""" |
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Method for getting and extracting data from backend |
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:param osm_id: id of the osm shape |
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:param radius: radius around shape for data selection |
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""" |
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response = requests.get(f"{_self.base_url}/estimateShapeStatsAsync/{osm_id}/{radius}", headers={'X-API-KEY':st.secrets['api_key']}) |
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loading_finished = False |
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if response.status_code == 200: |
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loading_finished = True |
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elif response.status_code == 202: |
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response, loading_finished = _self.__waiting_for_calculation(osm_id, radius) |
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if not loading_finished: |
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st.error('Error while data loading') |
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st.stop() |
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return _self.extract_backend_data(response) |
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def __waiting_for_calculation(self, osm_id, radius): |
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""" |
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Method for asyncron requests for Biqe monitor endpoint |
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:param node_id: node id for calculation |
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:param radius: radius around node for more possible data points |
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:return data_json: json with response data |
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:return status: status of finished data loading |
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""" |
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time.sleep(self.sleep) |
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response = self.__check_request(osm_id, radius) |
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while response.status_code == 202: |
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time.sleep(self.sleep) |
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response = self.__check_request(osm_id, radius) |
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if response.status_code == 200: |
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status = True |
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else: |
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status = False |
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return response, status |
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def __check_request(self, osm_id, radius): |
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""" |
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Method for looking if async calculation of node data is finished |
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:param node_id: node id for calculation |
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:param extension_radius: radius around node for more possible data points |
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:return req: response data from endpoint |
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""" |
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url = f'{self.base_url}/task_status/{osm_id}/{radius}/ShapeStats' |
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return requests.get(url, headers={'X-API-KEY':st.secrets['api_key']}) |
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def extract_backend_data(self, response): |
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""" |
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Method for extracting and procrocess the data from backend json |
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:param response: request response from backend with data |
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:return df: dataframe with extracted quality data |
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:return geom: geometry of the shape |
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""" |
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json_data = response.json() |
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json_data = json.loads(json_data['state']) |
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geom = json.loads(json_data['feature']) |
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geom = gpd.GeoDataFrame.from_features(geom["features"]) |
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df = pd.DataFrame(json_data['data']) |
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df['date'] = pd.to_datetime(df['int_date'].astype(str), format='%Y%m%d') |
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df['weekday'] = df['date'].dt.weekday |
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df.sort_values(by='date', ignore_index=True, inplace=True) |
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return df, geom |
