Upload data.py

data.py

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+'''Copyright 2024 Ashok Kumar
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.'''
+
+
+import requests
+import json
+import pandas as pd
+import geopandas as gpd
+import contextily as ctx
+import tzlocal
+import pytz
+from PIL import Image
+from datetime import datetime
+import matplotlib.pyplot as plt
+from geopy.exc import GeocoderTimedOut
+from geopy.geocoders import Nominatim
+import warnings
+warnings.filterwarnings('ignore')
+from plotly.graph_objs import Marker
+import plotly.express as px
+
+def flight_data(flight_view_level, country, local_time_zone, flight_info, airport):
+    geolocator = Nominatim(user_agent="flight_tracker")
+    loc = geolocator.geocode(country)
+    loc_box = loc[1]
+    extend_left =+12*flight_view_level
+    extend_right =+10*flight_view_level
+    extend_top =+10*flight_view_level
+    extend_bottom =+ 18*flight_view_level
+    lat_min, lat_max = (loc_box[0] - extend_left), loc_box[0]+extend_right
+    lon_min, lon_max = (loc_box[1] - extend_bottom), loc_box[1]+extend_top
+
+    tile_zoom = 8 # zoom of the map loaded by contextily
+    figsize = (15, 15)
+    columns = ["icao24","callsign","origin_country","time_position","last_contact","longitude","latitude",
+            "baro_altitude","on_ground","velocity","true_track","vertical_rate","sensors","geo_altitude",
+            "squawk","spi","position_source",]
+    data_url = "https://raw.githubusercontent.com/ashok2216-A/ashok_airport-data/main/data/airports.dat"
+    column_names = ["Airport ID", "Name", "City", "Country", "IATA/FAA", "ICAO", "Latitude", "Longitude",
+                    "Altitude", "Timezone", "DST", "Tz database time zone", "Type", "Source"]
+    airport_df = pd.read_csv(data_url, header=None, names=column_names)
+    airport_locations = airport_df[["Name", "City", "Country", "IATA/FAA", "Latitude", "Longitude"]]
+    airport_country_loc = airport_locations[airport_locations['Country'] == str(loc)]
+    airport_country_loc = airport_country_loc[(airport_country_loc['Country'] == str(loc)) & (airport_country_loc['Latitude'] >= lat_min) &
+                            (airport_country_loc['Latitude'] <= lat_max) & (airport_country_loc['Longitude'] >= lon_min) &
+                            (airport_country_loc['Longitude'] <= lon_max)]
+
+    url_data = (
+            f"https://@opensky-network.org/api/states/all?"
+            f"lamin={str(lat_min)}"
+            f"&lomin={str(lon_min)}"
+            f"&lamax={str(lat_max)}"
+            f"&lomax={str(lon_max)}")
+    json_dict = requests.get(url_data).json()
+
+    unix_timestamp = int(json_dict["time"])
+    local_timezone = pytz.timezone(local_time_zone) # get pytz timezone
+    local_time = datetime.fromtimestamp(unix_timestamp, local_timezone).strftime('%Y-%m-%d %H:%M:%S')
+    time = []
+    for i in range(len(json_dict['states'])):
+        time.append(local_time)
+    df_time = pd.DataFrame(time,columns=['time'])
+    state_df = pd.DataFrame(json_dict["states"],columns=columns)
+    state_df['time'] = df_time
+    gdf = gpd.GeoDataFrame(
+            state_df,
+            geometry=gpd.points_from_xy(state_df.longitude, state_df.latitude),
+            crs={"init": "epsg:4326"},  # WGS84
+        )
+    return gdf
+# geo_df = flight_tracking(flight_view_level = 6, country= 'India', local_time_zone='Asia/Kolkata', flight_info='baro_altitude', airport=1)