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'''Copyright 2024 Ashok Kumar |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License.''' |
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import requests |
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import json |
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import pandas as pd |
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import geopandas as gpd |
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import contextily as ctx |
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import tzlocal |
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import pytz |
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from PIL import Image |
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from datetime import datetime |
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import matplotlib.pyplot as plt |
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from geopy.exc import GeocoderTimedOut |
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from geopy.geocoders import Nominatim |
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import warnings |
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warnings.filterwarnings('ignore') |
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from plotly.graph_objs import Marker |
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import plotly.express as px |
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def flight_data(flight_view_level, country, local_time_zone, flight_info, airport): |
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geolocator = Nominatim(user_agent="flight_tracker") |
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loc = geolocator.geocode(country) |
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loc_box = loc[1] |
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extend_left =+12*flight_view_level |
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extend_right =+10*flight_view_level |
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extend_top =+10*flight_view_level |
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extend_bottom =+ 18*flight_view_level |
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lat_min, lat_max = (loc_box[0] - extend_left), loc_box[0]+extend_right |
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lon_min, lon_max = (loc_box[1] - extend_bottom), loc_box[1]+extend_top |
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tile_zoom = 8 |
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figsize = (15, 15) |
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columns = ["icao24","callsign","origin_country","time_position","last_contact","longitude","latitude", |
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"baro_altitude","on_ground","velocity","true_track","vertical_rate","sensors","geo_altitude", |
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"squawk","spi","position_source",] |
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data_url = "https://raw.githubusercontent.com/ashok2216-A/ashok_airport-data/main/data/airports.dat" |
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column_names = ["Airport ID", "Name", "City", "Country", "IATA/FAA", "ICAO", "Latitude", "Longitude", |
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"Altitude", "Timezone", "DST", "Tz database time zone", "Type", "Source"] |
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airport_df = pd.read_csv(data_url, header=None, names=column_names) |
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airport_locations = airport_df[["Name", "City", "Country", "IATA/FAA", "Latitude", "Longitude"]] |
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airport_country_loc = airport_locations[airport_locations['Country'] == str(loc)] |
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airport_country_loc = airport_country_loc[(airport_country_loc['Country'] == str(loc)) & (airport_country_loc['Latitude'] >= lat_min) & |
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(airport_country_loc['Latitude'] <= lat_max) & (airport_country_loc['Longitude'] >= lon_min) & |
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(airport_country_loc['Longitude'] <= lon_max)] |
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url_data = ( |
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f"https://@opensky-network.org/api/states/all?" |
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f"lamin={str(lat_min)}" |
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f"&lomin={str(lon_min)}" |
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f"&lamax={str(lat_max)}" |
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f"&lomax={str(lon_max)}") |
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json_dict = requests.get(url_data).json() |
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unix_timestamp = int(json_dict["time"]) |
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local_timezone = pytz.timezone(local_time_zone) |
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local_time = datetime.fromtimestamp(unix_timestamp, local_timezone).strftime('%Y-%m-%d %H:%M:%S') |
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time = [] |
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for i in range(len(json_dict['states'])): |
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time.append(local_time) |
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df_time = pd.DataFrame(time,columns=['time']) |
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state_df = pd.DataFrame(json_dict["states"],columns=columns) |
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state_df['time'] = df_time |
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gdf = gpd.GeoDataFrame( |
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state_df, |
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geometry=gpd.points_from_xy(state_df.longitude, state_df.latitude), |
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crs={"init": "epsg:4326"}, |
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) |
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return gdf |
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