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Fetch_5_0.py
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4111 lines (3638 loc) · 227 KB
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#Python3
# fixed an issue where submitted data had empty or null lat or long values. those records are now skipped and user notified
import simplekml #the library used to map longitudes and latitudes on google earth
import pandas #used to read spreadsheet data
import re
# import operator
import streamlit as st
import chardet # used to check file encodings
import os
from polycircles import polycircles # creates kml polygons
import leafmap.foliumap as leafmap # maps
from leafmap.foliumap import plugins # maps
import geopandas
import folium # maps
from math import asin, atan2, cos, degrees, radians, sin # calculates shapes and polygons on sphere
from folium.plugins import Draw, Geocoder, TimestampedGeoJson, HeatMap
from streamlit_folium import st_folium # used to create geofences
import datetime
import geocoder # search bar for geofence, api calls for address and ip lookups
import gpxpy
import numpy as np
from dateutil import parser
import xml.etree.ElementTree as ET
import zipfile
from functools import lru_cache
from typing import Optional
try:
import pytz
except ImportError:
pytz = None
import hashlib
# Safe session-state helpers: using st.session_state before Streamlit initializes
# can raise runtime errors in some reload/timing scenarios. These wrappers
# catch those exceptions and provide safe fallbacks.
def safe_session_get(key, default=None):
try:
return st.session_state.get(key, default)
except Exception:
return default
def safe_session_set(key, value):
try:
st.session_state[key] = value
except Exception:
pass
# --- Persistence helpers to avoid storing large objects in session_state (Streamlit Cloud) ---
import pickle
import gzip
import tempfile
def persist_object_to_tempfile(obj) -> Optional[str]:
"""Persist object to a gzipped pickle and return filepath, or None on failure."""
try:
tmp = tempfile.NamedTemporaryFile(delete=False, suffix='.pkl.gz')
tmp.close()
with gzip.open(tmp.name, 'wb') as fh:
pickle.dump(obj, fh, protocol=pickle.HIGHEST_PROTOCOL)
return tmp.name
except Exception:
try:
if 'tmp' in locals() and os.path.exists(tmp.name):
os.unlink(tmp.name)
except Exception:
pass
return None
def load_persisted_object(path):
"""Load an object previously persisted with persist_object_to_tempfile. Return None on failure."""
try:
if not path or not isinstance(path, str):
return None
if not os.path.exists(path):
return None
with gzip.open(path, 'rb') as fh:
return pickle.load(fh)
except Exception:
return None
# -------------------------------------------------------------
# Performance/Stability Helpers (added v5 PERF)
# -------------------------------------------------------------
# Centralize expensive regex compilation so they aren't recompiled each call
IPV4_REGEX = re.compile(r'(?:\d{1,3}\.){3}\d{1,3}\b')
IPV6_REGEX = re.compile(r'(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))')
def filter_valid_coordinates(df: pandas.DataFrame, lat_col: str = 'LATITUDE', lon_col: str = 'LONGITUDE'):
"""Return a cleaned copy of df with only valid numeric finite coordinates.
This consolidates previously duplicated logic (numeric coercion, NaN/inf removal)
and returns (clean_df, skipped_count).
"""
if df is None or df.empty:
return pandas.DataFrame(columns=df.columns if df is not None else []), 0
working = df.copy()
if lat_col not in working.columns or lon_col not in working.columns:
return pandas.DataFrame(columns=working.columns), len(working)
original = len(working)
# Drop obvious nulls first
working = working.dropna(subset=[lat_col, lon_col])
# Coerce numeric
working[lat_col] = pandas.to_numeric(working[lat_col], errors='coerce')
working[lon_col] = pandas.to_numeric(working[lon_col], errors='coerce')
# Remove NaN / inf
working = working[
working[lat_col].notna() & working[lon_col].notna() &
np.isfinite(working[lat_col]) & np.isfinite(working[lon_col])
]
clean = working.dropna(subset=[lat_col, lon_col]).reset_index(drop=True)
return clean, (original - len(clean))
def detect_and_set_header_from_rows(df: pandas.DataFrame, max_search_rows: int = 15) -> pandas.DataFrame:
"""Inspect the first `max_search_rows` rows of `df` to find a row that contains
both latitude and longitude column labels (case-insensitive). If found, promote
that row to be the DataFrame header and drop all rows above it. If not found,
ensure column names are strings so downstream .str operations won't fail.
Detection uses simple word-boundary regex for 'lat'/'latitude' and 'lon'/'longitude'.
"""
if df is None or df.empty:
return df
# Make a defensive copy for inspection
working = df.copy().reset_index(drop=True)
nrows = min(len(working), max_search_rows)
lat_pattern = r"\blat\b|\blatitude\b"
lon_pattern = r"\blon\b|\blongitude\b"
header_row_idx = None
for i in range(nrows):
# convert row values to strings and lowercase for matching
row = working.iloc[i].astype(str).fillna("").str.lower()
has_lat = row.str.contains(lat_pattern, regex=True, na=False).any()
has_lon = row.str.contains(lon_pattern, regex=True, na=False).any()
if has_lat and has_lon:
header_row_idx = i
break
if header_row_idx is not None:
# Use that row as header
new_columns = working.iloc[header_row_idx].astype(str).str.strip().tolist()
new_df = working.iloc[header_row_idx + 1 :].copy().reset_index(drop=True)
# Ensure column names are unique strings
new_df.columns = [str(c) for c in new_columns]
return new_df
# If no header row found, convert existing column names to strings
try:
working.columns = [str(c) for c in working.columns]
except Exception:
pass
return working
@st.cache_data(show_spinner=False)
def cached_ip_lookup(ip: str):
"""Cache individual IP lookups to avoid repeated API calls during a session."""
try:
return geocoder.ipinfo(ip).json
except Exception:
return None
now = datetime.datetime.now()
st.set_page_config(
page_title="Fetch v5.0",
#page_icon="🔴",
layout="wide",
initial_sidebar_state="expanded",
menu_items={}
)
logo = 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tUOVOprQ5qcL8eZldJYjbF6lwfRW518DTLU2fjZM3lTx7sUZBOWUZ983IQWtOq3a6mqGTlBEZSyd1U8Vp93477mf7TRgW9GpgCgja+a6tYt8QK3l+LKYZ28sxlXC1U8PiDZwZx0aFS0Si0UELxCJlImmCoSZGJXqyoAd5bFscHVRGsrGUGDMZJ9eLv89new7q5sH/POIZ3d2NgMUGK5+Ls2DFjd7IA0hS4lx12OyjZgHTDifm4/ngLkJZWJ2hDqsVnVVGY1Tf6J6lD2UZtAZJQPs4oXE0c0mETnC0sGV+tq15iNTjlW36OJVfjaNjl9b+RxMRZ6c4Z9bj47+ws38onMLkJTPlmxrn3o+xR5Vj5LWQGPwdQXciLp2mcjFEmTj7llms68kHGThm6l9LQLWqkGtGWPWlHZdqz+9y1cby5JEqfGqkZ6cnqMamTBKR3lli/13K1QFN1itHQrqbWiuq6BjcK2dz6bK+47qiO5js+azEN537aUzSf2ySAFz+bqDJ6yNvU7+xrtn5PxKUuJVDTZNVTJqaUIs3l9oRmbEgEpLsn0u5ziBtRPRPJk5TaDCBxTqhr9OL+mVFc/XryAuuylKOlttknLj3UgzOG0Sev0kWbFO1OQmtSy5PZV3b9990KSjYg/fzEAlx/Yi4lJGDpxgQm0w/JAiQau4+XykZxlcDjMR2KPYtdxx1fCQ/HkjBHfdOryd7N5YUEe4Daj/1FQ02TSMLTl7glg2GYInGCwEQj93gCE0nAdMkzCfz1LMvGdPupLINNe+9H2aXKmcpyYGlG30wj86XPcfklgySXgK2X4NtTvLEhsE5fcSXqhy+L8a0HT3vKaOsaLVPLf8YmT9I9YN66BA67v7ndg08qmxxr17JbzP+vIIZ0Y3+glKLzBhAonUz5MoF/f7EJlSUuaNm9fQObZgE9Krtjg6ulnnZ9BaS5cRq1qbJdcbjPvGKylbFwu3wLkLyYRyfgi/7RjI9XxmnABopprA6x83KBk2qaJYBKKJMzpVS9TfSbfGBmzLxG9vfg2lF+gqpVqgFIuxJ7yftuAyUbkK4/oQDXnSAbkouAFMe5j9Xi06owV9/yjTuAFFEx2uUOEHya4Q4vp9jKWT14DOoKRiAaLEQ4rxwb6SzRSC/XHHayIMGrKNGI4sg65NV/BV/dDIIaccrXi53RR2AKEZho5CYwyQ4lR8pLnkESmDy4/VSuyvG8gnyVXSC7gnilu0dNvSJkjFZYrPk6jR5IvmqESLVt2onz5A5LZxkBo78B+3DQb9bAtsbIDn+2zZfJ+iizg5wBbWkj9ToNdsm+HaEY3QlEKr4tEjiIGulYq4ylHSEX3QHaki7l07WWFS2nBH7DaCG+Jem3lpC8xsXlwLst6Uh9bQ0zYNSBICexUC/+1IZ5W4YK0YG1G21UQY7Wfy6L4dRlRCkSgxl26kRpLuxih90CSjYgXUt17YYTc+CnkcAA0uM1+HhFxHIHoISkAaNlfZcrAHdoOVyBbthUeRnmeYbAW1iCges/QGn1h1joHoeLn/wSX834CEXlPaiK5aN/7x4YXtkXB1ZMwEEDJmJQeD4K1jwOV4Qu+r7+BKZmAlMCv0m6Bchf6VIC018oMfXgqpwBJjbe/QaYssnGZA2tmsaEca6kjx18afp/bJmy1flJbQ0u65udHFmlRHJgy39mcxuSw05KsL62gUxjmLS9+kTpDGhfal25/aPU2yDdBxqtcdvmhbZt0sfeL34mTT1tXquTSew0E932nAKVolmOpvdM9KN3scKZ6KaSLFgqr58gvoqOkMc+YEGNkhHavnLydtgRBegkKTlZr+JgtVn230iJTOf3Rup0UCqhY6SYLwnphhNyCUguLKuOGQlJgKTVt+t53ppZfLQnboY7sgZ1FRfhY88IvPLVatw5/SW8fv7hKKv/i2kZb84hCCj8oHYFO2ADqudXYz47xBvJFhp29HE486RjMXHordi//lPkr3mMLi49CEyUqrwx/HZCCa900ZGyHt/7hwt/OZPAxBnujglS5WxgyjKJKTntazEsq6i9aLELlfbmtm/wSdKSAVlq/g5JBn+SzAAdAfgklm9TtDdCtY0gOH6IBycP0rI9jfIpSCfVMUp16+ppYdQTk/qwm62ieqlabA+It7lJ8oRUXPlhRpmJdW+lTgUlG5B+djwByUhIAqQovv1YnZGQbuTq2w0ncBWMs24U9OSOLUfCV4mvB/4Ejy1oxl1THkT1wnn0ViunL+HR7EGVbI2lNHaze3h8RqzPCeajsKII7rp6NDc2o7SsCJsXzMavP3gXj+x/CK789uk4d+iNqFz6vwS8dfQ6rUDQE6FbgFS5OOSvdClVtwckMQmYTi00bW1JTFkGTHtrL8zQc2mAa2OJ3U05HEVcRMOPjuakR5tTWFKSWZGRKYIrbQwefHZ2AlNmxbaYB9IBpNTn6iiYpf422z9nfN7VbCVKBaSbT8rjkrYbKygh/dvjdfiQEpKM3ZKcJCEZQIosRzTvSLxX8SP8+B9f4pabb0VO3ToMHdAPKJLDo4xEFIeSJNuQlx2gqSmExYuXoM+gChx14jGI+r2o2lCLIYMHI7pyAa697kZc88ZyfDLgBsrqIwh8KwhMfgaZJoy/0n8ck4cX5jXj+89spndzHLkUw/9AG9Olh+UaCU96v4QU+7ns+zvvDgfEAQVRS+oZ1d+NkRVasm8BJKO2MYhz4ybgZ28xDSppQ4PlnbU3g4p50F04ZFRS0sDVALYB6ZrRBbiJgOSjRW4Z/ZDOe6IWHyy3AOnnXH2TcTQap8oWp4RUdDTeLj0X19z1FGZ/OAMjhgxEqJkNGaIl0u1nKBBLJhDxP9+5ssblifX8emhFLn78wxvR2+dHeFMd/GNPxudLluAPd96Ffv36YXBpDzz90IOoWj0ev7/4BzhyxX3wNM+ls2s5AyCpysnGxPLu0sYDzwJ/PrPASEx3TrRW6x74NBuN37vQ4s5PM8qBfBqENlEl+/Z+dBuQlETVTStmIrNSSan+9UUxfMN4sL4UwmXUzufE59D2OZAxSckGJHv1SoD0izE0anPlbDlVNgHS+wy8vIESUgsg0bs1UUd7zxC8UjQZp/z2MQNIKBuIrxY0YtEKLuuuYhVXehGTsxt1c2uVh1KTwIn04ysvQ+k332DtlEeRIBhV3fYrHFVShJ/95IdYvnw5l3EjGDRoID54/VX85C8vYEa3C6j+9eRCRxMd2qwVjd+Nz8OVR+Xh+a+acNmz9UZiymNEoYDpEkdishjtHLfhgEJJlEZEdMIAIhGleYWNiNQ95WzZ2OzCXz+zLtrESdSRuA17dnjIiKS0NSBdTS/tX4yRykY8qY5vASQZu2/cIiHJGEjHu+bNWLrPdShw5eOFK8ZRJTuTbUvQEUk6YvPGaD8a5KtBrGa1cSJWnNDq2jp87+LzEFiyDOvnzEH3yy/DwrXrMOywQzD/qp9h+K9uwmFHH4GvZ85Btz490Lu0CO9/Ngtrv3MWVlZcgvKFv4Y70J/L481mqfX3dKTU3e5h6InmuAdo/O5GG9OdEyzP7786EhP54lAqB/IoJdXQwH3aPvS4LmEAFA3sdmiLMhp46a4yh57a0xbHodU2+TflU5pyaMcc2GVQ2haQCnBLEpCW04b0HUpIM5ZGIHeAm05iJkmKOhGCjpt+SN7wCqzs833kbqrC8et+Sme+g6iuaeFT8i8hQihhRGF65YbWU1rqC090pWl4+aUN6N0LNe+8ix6nTcKfHn8KH7/7Pv7zv36A/S84F6GFC3HAvsPw6Qcfo7B2Leq7D8CTV30XZ62/C1WlJ2FDj39Dzw2PIeLty1W5iAGm2wVMLFeOlLrtnw0weYzEJBXvwZlZ6GDJejq0ZzigiHxQ+jmatqQAbaZRBjq7XK2H1PRlpgPvmQp20bu25mAHH2JrQPrJcfm4hcBjSUgxnP9kHf4pQKKEJECS6iVAkq+1O9GARv9QNPsLMWjx7cYhMN7wRRKEaEZK1iUpMzHvDE/4+5uzamblT4owgt1XUIDQ+g0YdeThqFpZhYEVfdE4ezaKDtgfEUpTonCvwfjrf3wbpzW/CHfDHPQKb8KKigtQUltO9ZEOmpTYFMQbpGdiqiOllnXvPSMP3Qs8+F+qcnKw/Ntnjo3JMNU5bOmjB/dhb6UDr3q23AY0l0p1ayBgvbLAUt0aOdfWb8fx0mFlaw6kDUpbA9KPBUiSkJg5byUlpPOeqMF7S6OM9M/FL5KApNgl5T2KM/OwK7QGtZXfhad8P9TlX8u0L0GajNisRBwNfpGZY7Tq5mbMW/VC5K99wpzUyls+DYjzFi/F4IMOxPzb7sTBN1+LQ3/4n2iYNxdV77yP4lPG4/2XXgH6DcU9AqTwq/DU/RNRTwmlNcY/lQ1DQcNJ6LH2YUR8/VivkPF0zqUf1R/kr8SK3EeJSZW4//Q8lHGXlD9Oss7/zZGYWveif8G/jOuBEIg0pIQfGEJg2ZPYd7UQQ2u3sgS8vSQOhdDIsTIbV9w0jttLCofZHZQWKNmApJ1o5RgpQLqVgBQgIFXVxPEdSkgWIOXTtpRPELAkJANIbDCuo9LCXIZv4t1w/1PvI8DASLNQamGRAQL74d0EpXp3Dn1AhuBYAlFcEw9BqYxe3E8+/TzG/u4W5PYvx5wbf0VPJ3oX8zX0vMlYVlONRYuW4+m7f4NJoZfg2fweEgQ+d7A3Pim/FDdPfR0/H90f3P2bxVmBmQrWlPFbwKTdUuIE0T9/0mjA8T4BU1JiEig+TImpnNkGtFGm+NHZHc5mjcJn0mo0m6EZfjfzh5k9MlxwlhenKBslwRtU6mYWRwERpSUuxIjsVbcFNZYXZ4HsSOl6vZsSM3hI1lGpfdDAnPC0+7aL+GjUUA3ZHvHt+l0aF6XVvzUA8+lRqq2xfzQybytAqsW7S8KUkAhUTLRmvFkVXCsRSEQjtTtShVCP06ja1eDxv/zZOr+T4xUjfkSJiVIOY9xEXkUrkuauXIWDT5+ITX+8Dy6qbgUrViI4ZABemzkPU++4mYD0Mjyb3mOALu1ZgQp8WnEZrnvkdbz18suYtP91OKxgNCWo6Yh7enOmE2DSrEWDVQ5z2dxJYFK15Uip2t97egGBiZ7gkphIAqae/NjYqR1OdwbK8lwoVOwbET6WhjevStGGAY3u9nlHm5tu78DClNtIpI0OFA/WUVJ9tHKVbiL+jt4v09crgb9i9g7sybza/jgXZxgeZXpJy52WVFttZ08oLd/suU8xZjxgTzDCREdrsYnPK2q2NFLrj044pgVKAe6UsJqazYWH5Jq0ILaEdAEzPE5fHOYWSHIHyKOERLuPUdm0kEaRVijNOAmq36gO9sPMRVWmGQcNGkQ3/DaelG3qY4qSBa4iYpmq2nJNlCHVWhd7/rVpOPSKS1E8oBy1S1aiz2kTkbPfwbiuRy7GhN8iIL2bBKS+mNnvSlz9t1fw7muvGlZ+sngtzj1iMIqrp9NDnFKYlk8khLOCYX7MkSpHR0rV/YFPZPxO4J7TCw0w/ZE2prWbo2b7ppKc6k4MjFRgp4d5te3wzF3r4jnearQ3bMMwqa0DqxBiBkXRNxvauqD95wr9nS9ltr827b9SqbVF5YUekwlSpgkLgkwPMurc6nrLImoWky0Mt360p46sYJ4vjhKqFIf04gYFrJ5d5/ZUSYKF0reUc2LuTOowKEmXjicb5HpG+wcY0biKNqTzn6zBO4sjkDvALfRPUqNZgGRniEw+huxG1LHXxgsxa9Hn6EGFu6mpiQO/7cFmJSaPJpnnViolQy4yqBeDcRd9vQDTPp+N0aefjjV33o3+Y47FqNAnKKqbSqALmWVad54A6d+3ANKQIYOxasFCfLmkCtVH7QemHKbIrWRyKtyqhxpA9c+jzCq3AAGSVDnRvWcUoJSq3C+ZHfO1BRtNqow6+qzkZThA0iwvEyiLGabw3HleqplJxptatP9gBYQmODu6ccWLzchnRLtyLadTX2M3ocSmXTiePMfKFmAH57a3RqpPkIA/g0nsb+UGBmUESklMXYlkkhAV5/CDJltjlrD6jtpNmSIXJdU3Saj1VJU6W8W3atT2UWEvMc5rcl84dQgD3tmfZZhPhyQlK/tBur/f2T07DEoydmlnhqspDQ3u5mc0dAy/fKdhCyDJtuRhiynNhsa4wMZOSGVVhqPB2xvrmPd56cq16JdXnCL/bL+6xvjNsmj2MQARaqJzZdU6VB5+LPYZNACVA/qi7rLLcUjeWhR/dSc7OaUsFhfrNgYzu0/ENQ+9iumUkORIWV/fgKLSQixcvQ4bo34M0YUKYUm2kQWQSiVK9YKqnIBJNiaRgGm/nh46gObjIA5MSYsPUY0rY+ItW5YxF2boICkpl+rBaSNUyTRJA8YfxfI1FqgpOt7TInR2qFBLHZctJYFB3e3p335vZ1GSSLkVh6UCMsiI9ekM3rWzNmldZnttlwb5LFwalh1JWbZTKSoDYJaR7EKW5SPZ2dOqH23AaU6Q7bldh0HJqwxabIJD+rABmI1vIZNwKaXsxGFB/JzBtXpgGYvNLN9WDeLNSAT6ol5J2FdvhLsvNwYggLWLNCORmBgUPQcNwYUnH8XI7BIM3/wRbXZzUHzqRPT87FLWixNX99MwP/9QvFvtw0P3PosZ06djwMABBpA8fAZvIIi6jbWoY5J+uMsNKNmpd22JSeAkYA1JqmBrKrXuJ1Uh3DiNu6QcmoN+HJRHVbD8z+QZ3jmgpOdVBwhLn0yT9PtA3MsVIAuJtiOUtrt0deco2y8eSw/Z4rRrBNmKzUkbRTtbv9312x0Xql+I1O7GHmH9aY6SQrT1kWxOIgrcWUPK9hlX1tZdIEnL2x3fu1Cu/dMOg5Ii60XFXGmTaFFVZw2WcUP9jP3h/mDMkijVzR7g5mIeZJeRmJtIhKm+5SaNZTU05dCYrFzN2yFjfOZ9kn3AqG957jDumTAMg6rfhXfpC3Rao8GV1Tqg9h8I956MefkH461VUTz94sf45ysvQmbp/pX90djQSJ8kdhl2ErfAdVWjtbGeJ8h0E81GwrOrYauTAiiJ6pKYymjEmjQsB1+sqseazYz47unlbqdWzTq73xm1ya5cB9+pTBjeMx09f5khmYRt6U50uPuYmick7qpjmzwPHXyYbLs8afC3q6V+oB1XlEWygbuJ8C+TtXlPqm523fRucvPtcv4bqaeppWb2c4d7ldndgpISszMIk7j6Yg3KxRtZS452GbO3BSSdV8VlE+ItiSJ+zTAkV5RgIDF3m+lbhkMCAkEMXDmTdxMSIYac9KJv0WPovapGmzwwrwxVkWAJGrpPwjz/MExb3oQnHp+GWdPf4t2AgQSjMI2yoeaQASRzUx1U/3wvaNogaZNDXW2RZZS3P6ubUZWj8VsNMZ/ZMkXmuVlngZXIehrzMeMHuSp4bH0hrdL5bGR7QLtjkOxZPq2ikj/SKqBJXGQb+TpQmFeiP9VJbRWklaDO5F0HqpXWpZY9raXv6FmMZMr2KsmxJgCFbW7aDW4jO3sA5STfwNzg1Y1KNa2adlRioppKoUS7rZQXWH1pZ/dM5/sOg5KNHYsEQvw/jCrMMf19uGNGA44f5Mdp+wYYKcIvkl3NkjRk7JbEwQHNrJKI1DDZG6/ptS8W+vluIcOW37Q8SJJxrkLElKckyUQ3rUUS2Dy5fbGp+yn40jMYrzDR29TXnsfSmR9CKdwGDqhkxHaUAZEhDkLCW8pI1OdohC623cuISyyI6UYTBD52J75EAh6rznqXSc9D8e/Rmc14YlYjJu+fw1gnso4z4Tfcp04UFUK29E1zblcPqo0G/8ZGN/73w4jhocRmGYo7SipnXaOei0vxfPQklna0GHNvL9vs42VuboZIIzwDl+29vtpTmGrODOxmQ4d56y39po7xY8bJrD0FZMk1tkqmVMAS3y1J1u4/7DVsJy+lcjNIsqDOyuvkoW3lxflxXPws/QTTIgtkJ+zjxTPn+jlstWVmx/vizm7dYVCqNyISMOWLZtpVgijJ464g3Eft2PuqcfrDNXjpuyU4dUSANhDZYyQ12YZubYjH6riCjGNbxjxJRXjz1xfDG6Lbq0DDBgSjHkpK0sVSEVgGA3L7cqfReLSel3LLJDpTbup/Jb5wVeLFuavx2MtTsXbeLPoMESwGJMGoicnmtwIjFmhI55s5cw0ZfABKmIkSkU10OWBeCc7aRs0UDBn0tRiuXFBTPwvhgqdqze+vPyHfrDpWcdXxTyaAl450bGePcC2TZMAnYfymtM12Jogatsm2mK7Z3DgG0qa3nGr7Hz/a9TpRWE1rA4NM8GJXyrBt2DXM853s2K2Kk3SrtCYiCSVZob6xHtqrTaRsHrXM7dQRyqGtdi0XueTD15nUYVDS3vO9ArWYyWT/j38RwhXHBHHMgACmfa8EY/5SgwkEphcvLMWEEcxvJGDiHGLZoTRrWAzRp6KmlRhQNxvB+rfZauU8Q3AgiLVW43Q9rxYiRzcwILcffNHlWN7rh7htYT7uvu8OOjytZgaAYgNGIW492rgDMGJhhmTo3sAZ7ph+vVDGjaKNe5Ix4Otrq466ryQk5RPXc36HYTOij67shgMU68Re+cCnTdzjPdHJDpSS8mLGe1yqojQe4/diatOBAzuk7BwZWXpnWco5LRpcxh1BmtXO7Sd1ac4LZgFB6V13h0d8+2vXvittSUmqkNwBUg2/kmQ95M+QUkuKlo01Q5a89lVue1elYImk23rOKVYrbu8Hrc8z6YEhW1tq/W3m/uo4KPHeDa5iLoFX48rn69CXKRsmDQ/gJPo+TPteGYFpIyY+VE1gKiEwBRBha0iFEzDRE8gE0uqhvE0bUOvvi16MC4n6LSfKth7WdGAeXFpREwfZB7Rt0vOvvYkyAlIxHS/l59QeMLLZ5pGSTzpwQG+URhYb47lmNiOcmW8Emxx4lJAe+7wZ5z1uSUgfXlGGI/qTZex0D3/WhJumbe5kQFItrJ6kga8JIUo/MW+6GwdYj6VCd41YFds3SVs1a2eUtCgZ4pAVUkQHH0C+R6IlDKtqoBaaQ6dEe5ncSJPsYz0tLxLjWmJdnT1HjbWO+qklmItc1LEpqOPPnBYo6TbayLCIneq0h5KS0XA/gcmbAkw1eJkS0ymSmCiVyLAmYDIqnbcQhXVfYEXl+cgvUvgIQcIae1s9gen91vfVSxDYOG3LdT1LS7CEVys7pXyYUm1GWxWyzZ+JMHWtUqbPZW7v4OZZTF/SjWqiZd+QhCTSnnSPU0VNBaQjaTuThDRlVgQXUpXrwdW49dQ+86yok23uk9ETNn8yBSwZrdy/XmFJ7wp8voaxmUSoPO56Kkl0i8REO9PQMinJ2mKbbx0RJf/12NnqidMGJYUqxJmfuIj25y2SkZGYfHjt4hKMe7AGp1JievmiEpwyjMDEGVXtYpQiVwkCTXO5wWQUv17SHTVVS5gyl/5Kxp5E8Epep5oqc0CjJweXHHgwjqp5SacMxbiiZsap9IAOkI9os5QZKSdMPgcH5NYisXYpRaJ+LCHEzmP1HhuQ/i0pIX1ACelIIyElMJWAdD5VOc2CdcS2js42Haiqc2kWc0B755XRkXgDbSx69SygLsTdckTG6E0k6l+sSZh7u9Un6CvKnVbSlXCzmA+dUbW0QclUhupEnCELFjAlJaNhPoylz9JrF5cSmKpx6t9q8AqBafzwoNnlQdZ6NzdXjHHG71f9DkrCB+NX9zyw02c7946raIwu48RTZUBLjS2hQZjUfimJNpkkhp191HD0rnmZLgYMEE7xlxEgPTErxA0OLJXtgx+U4SgBEme+qbOaaFuqM4Ck1Ka+dm79s9OHcy7ochwIMqQoyMUXZlXGvA0x7Nunxatb0pIM4T1zPTh3P9okv4whl0bv2ixwC+gKjN51ZYDbK8vwKlVOktEr31Cv48AfO5QS0yWWreEUAtNrX3MzSPo0WapWlFspVcC34U2cOSgH4741GUXk1j5Dh6CSfkWVlZXmNYDvQ/v3BSr3pZ+ODBCSoUSUaEwOE96KPk8uxri1/aKjJjdzNy+6AARoqVu8bDnOufAijCvj/nK1n9BOVU4JialLaCfy0Yb01OwQt4CyjNrvC5AqCUi83WOzm1sB0i4HtVoP4hy7MAeSMcn4dBU7CCWnVAdXLcF7AwmMH2zN+wql6Yq2sz3RPLsOSqq1JCa+GWAiAL36tYDJZYDpVapyovFU5177mttnE5jUmG66ukfdeahc+wz+c/wRqOtZgYaaDQyiZfgB7UQhpsVt5iskg1QobIzRanajszGVbgP3e9tY1hO1eWXYkN/NvNYl31v+1ndlqMkrRU1hDyyj7/+Iw4/AlaOHoxc3qIx4KXlp31HW1edL4GnakM6ZagNSKY62AYmSk2xLUtm09O8AkmnSf/mD7Kqid5bGrH7h5mRrnbIAigsyR1dYorm9wUDya+dtBxywYHwHF7T7KwETbUyFVH8kGRmVjbakcfsEzGedM8BEkBo7TH5M9KKmOuZqnIvj8z7CHf9zGa66+gZUMhaOGddNKhMrBk01sJvaTEgoSNTgkYtPgOu8IwgoO8JVq0PEaUj30DYV5VZNTQW9cdj8/0MivJo/7UvJLWxW2Z6ianYON8kUzbicgCSjNm/7eCtA4lJvrgWy5kLn8C/NAWWT1ET10YoE5qyP4+h+VOFoAVdEvlHhGB84uDSO7x/qw59nRlDm6nrZEPZEA2cOlFR7AlOCwKT0nwKhV2VX2seP8QQhgZTOyQAue9NYng+FqZB7+yO45mlcUFmGhl/ejOuvuxEVvXowi2WOkZakCoqMmVxhJl6Gmax7Er2rX6bgxOpraUPeaWaJgxeaz/xbRnP9lkCEGO1DzAG1bNDNyF35CTcqmIGIvz9/02gAyUhISUCSynZ0JR+AP35iVrOxLfUwEpIDSGSKQykcMOpY3Fomf2V+zEhFqZ7dZndcrsqdf6DLgFKQ3TUpXKWU4nzcmgM7EjO2vrZ9fxOYqA2ZNJvjaeh+fb6agbo1pSYBk0gG8Ne/oY2HeYLisSYkgv1RuvQ+XN5rHW7/7a1YsaYRy5YtQ15ejknqL4lFfpUCGaW7oAKIaONq+uys4PtK672B73pt5rkG7rZL58xY80omsllMv6Ygvur/C/hqFqH72qktgMT7P01JaPJjllF7xuXdkiqbBUiyLWnZv557dzkSkmk657AVB6TOi+6bGWXSP4Y8y7uF9kmRncPoqL5unLMvd27epJA/C8TMBc6hTQ5kVlJK3iIhYKLElE8hZdxfq42LgFS2rSWmN2gIH0OJKRxSOpP+KFt2Fy7t8x30v/8m/OHvb+H9115GX4Vu9BxhSUCUetyMm2vOPQyoGGHsUsIqkYDQoBYhS5/j9EVSPFrMX4LV/t7osWEmulU/kwSkJrO/+99p1G4BpFJ6pkvy4urbl000dlt+SPUhB5DEWYfa5oCHK7B9EtbW3S8xruySIxjBwMhtO7eSkZaYwOrqkW48OZdB0QQtTdNd0Yu9bQ5k/myngJKqKWByC5ioCUllMwBEVwFJTPL2nkiny5MJWPICP4nnBUxRqlR5q6bg9OJFGH7BJDx37KGY8tI0rPzoA6YWGU8VLAx3tBFVJcfg/kUM/N1YRQ3Ob1b05OMUY0pdGcgjTI3ZrbwcJx5xCIYl1qJi7h+RH11AQKqkVkeVjQHAz3wZwtlTLKO2JCQDSPRTempO2ABSd0lIDLh1JKTMd7q9rcTapLR07VshTKINtXsebaJczJFdSdKSUuscWu7GbWM9uOb1GCqLq80GF+kCU7SRcaCcPPdWv6dOA6VUYGIbGQAywDTEhwkEJnl7y4VAYSmKmztpCIEp3MgtvPvBtflDDK/7EAPKz8OpV07EtDNPhbdHEcJVfZm/bQMDeuvx+7ueYtzb4tb9u+8QTDzqIIw5YBBG9qSRceE93IjybcQDhQhz6R+xBmND+rsBJEtle+/ysi0S0tM8fw4lJAGScuF4chyjdmsGJ/9KqtH6SzO/P1QtzVpadodJv5M5UDvNdtUlcw/dYvoELWlpypfaTINRkynSkmwP2hnnh0f6GDPqosQURSW71sZYx8JzQky5XEb7Jt2iQF9kJBhyFNgLHTI7FZTUQyUxeSkx5VJikmRkS0yn0MlSgbvyBlcgrwGmoXKwZO909+b+bD74Vk7FgazhiNJTGEA7HKvLT2cKn1PQkFuBP/28Epuq2SgBH4qYWrUn94qpYGrSvhSlSzbPhHvdG2aURHIq2euZMYCOJPJDemaOJCQLkN69rAwjB7JibOCn5zYbVc4CJNoDHEBS87VN5Jc8mkWLLWHT+mMXj+lKDrt424z8fDVjOLvT1HDVK2GMrAjisAov4z5jJoeXAV5m29QOKPdO8iFEV4HnvmaSwOIaTn4J5jfaPiALiPIYNaHxs4kv3Uc0qJQ5wKuZxobfy5Fzb6JOByUxKy5g4jY8uezMlsomyShAicnHVCfMKPCwBUxvXlqGEylJhZkexQVKTb5yRNiiiXWvoBdeATO6IuxXupEChogwXq3czxmJzpORWm6TtIZ5Fb4G/0SUv3EpdERBtlTXeFsjIT07O4xvpQDSqIEskDPY3wlUk3m+BZD2rkbObIclz+hj1iPfhbNGeLkQwGUHsjk9YpA2DYCNbORnvuJSOieChk7YgCG9unXsV5LyosmA1R+8FMGb3w2giLmmlGDQdhEIU3oqoZf3I2f58cv3IrjtPYamkLSY4qN3uKKclBla+fyMXVQdl+yWFKmX6KYTfLj4IB9/HyYoRZn5dO9b0eMj7x5KMEUFc94hhx14i2REle1UBuy+wBxMk5jy5KQHNsIGphA3FvDSGU0U95dzvc1Lh7QoApGlJv9RnCsZGgtqPCUyDGsbJvoduYJcnaPvkZvbccf1JX/lp8H9WQLPWUkbkiSkUUZCEiCFjeSkWa6BFkiJ4g5tnwPaxSIW9eDYijhGVcjLXm2UXjdSsjc3XTjWMzbs7aWUBDgI2XxdlqI+pvXhziwzq2K46tUQ7pnoM1EEYeYyFzDpFeaMqb3yfjPGj7GDPLjnE6ZtnpsUO7fz5GcM8+CMER6M7udGpbonu7Wdrlk9fG+j9HpTmlwQMJkZgYNfwGQAiG74E5ni5PkLSnDaIxYwvXVpKU4wNiYTKce7UfxRAjZzX24qyURvCY0Hto4ahcOE70qNwmviBCSCka6Vq5KfHeD5uQQkli2a/v3SLYD0zFwBUg26CZB4CweQDIvadVCaDiskWiiiKb3jlGAuGrmVxSQipFdEx2/aib+Q+intqrwQePDzGDcV4PZcp3BS5HuEmQSUH16grk0XZGc6cYgbI/sFcOPoBGavjXOXoCjqQm6m0U0gl0GaA7n77kAmY+vNLBTaW1GdWpKX9iXUZEvOZQXbVBuNw0zRbgUlVVrAFJCoSmAykhFX36SyTUqRmE58oBrvEJhGU8WTKmflE9bsoGBeGg0ZV2Q2trS5YM7bf1jv8hXx+10GkJQRUzT9+2U4brBsSFQXCFTfetQCJO2qkb2AJIDNZJNb/HGOmcdBqXACJvkj9SUw3fNJxGzeePs4H/ow75hW4dR3JTGJwkwbrc/79Y5hP+0OlOAMqpk0NR879Tn+xERAyDHTcs4UGGna3Tnpqi3CZ0a7EVOysOTOmEsy7zy5cz5RHWOENSUdYgZO4urbWwvZWpRujMREdwHR8QSm6QusWDlFXFtpavVNW1W2GsgWaXW9YuxeIPDYgCSQOy5pQ3pmTrMBpDJKSAIkV5arbEr3stfjUgYHTHsHSgZvqY5pSMCkclcSmPpQwnl8TgyH3B/Gmwuo5DKDqF+b7pHkvySAUV2jEa8BHeWUj0ZphyJY2S/9rW2RdK2Sx8nNwM1sqD6T/3vn3aKt0WIqsMsHAWTnpHvrvDrv5KEFTDmcGLRngCSmtxfKpYxbGDEn03O0MYkETO/wvADGBhwjJZlvWw5WZkurK7LJLECiDUnqoOhtqmyjJSGRnv2Kxm5KSAKkUDYCEkVza0ZU4jqBkZ7L6rzmAfbEIaVO6oiZetnPp0EsDS5TZIONeddihwayqTcBQaqPUX8ydbdty7GBaRV1uZ40Yq/dHMeYh5rwvefj+LzKqoufvhRe5ro2m2mkFGHlQLdOqB+IR7Kten1R9mtL5lmwNsFt3C07lJ0BM6UI89F6dg7v1GfPII91E20lJdK9DEDZjDdn0z+4Cgq51ekeJDdX5ZQsTWRsSQY8qHbRAH36o5ZL/nQapo8bpFU5a6AKhEQWUGm7besRtkhIXxGQ6JwpEiBplxX1+ucoOZ3xaG3WAZJS3HKLdm6F7cK+9K+SzcEMWD6WmwOokT5Tb1QVm+fZXcvmEdZJe82PHexi0GlKnUwtMnSwn4+Tw8tfJ8yGBrv6fPq9cmbJj2fSPh7uT5i0WfGcVHqvJ4F53EBV6Ua4OJZ+auF2sMB+lgRzjhVwWd9ezj/vAC/OGu7CEeVe40YQ5KaurVQ2u2yqcsrQs4lhTktrXZi1LoY3FjLz6WxrAUibLri2yuklFwJ6GWDiUG7xxF0ijL0u+ew+9qWFdCN4nwHEufytvNE7Qs0sW3ztW+TCKBrdNe74pyGNyChXst9ZmsBaZmOVsJGuq8IeByU9kYdLqbXJJc8tIEL8fY7Szhk2MBFcjhvMIF4Ck5hhSUdiBUeOcJqgI4nqRQKPVvJEBuQIZsok8PxXzUaVMxIS29RFN4VsInnpZltKFAGTr4s659kDaHttrPxGu5vfgajlZLqefkk2jRvsIThxH7VCD7edcqOQIBqij4VSnaxviOPrDQnMYsrdL/iySSmCNAFvDUj29yG2G7P0bJfSASS7sJ3xVddp8WJXMrJmBSjpQVKByZKM2DrcOdeWbnSNWconyGwLTERmNlQqIFkrezJqc5OBeZS6CFTZCkh6NlGMwKRtbFJVGRt2tdy8J0gxjDKDpNYpk/XozOeTFK6dRFLrLiG7Sa4fHZQSMvnMPk7CyhiwkWl0kzuW7bR4+dDRlGR2lq6hZpG/E4fJ7T17JkwWAiZJfqlkt2MmUkRnDSjpAVsBkyQjSTmUmJ5l+EeLj1EJRg1SPiZOBRTHtQqhbAOpgGSHrei3MnbLtlQqJzOq4dkmIaU2rD5L5N+aZKPYk9RWnTJZn858vrbq3pn36whfNLiVJlemIgOe/LEGt1QivcuRUo6p6QJJZz57W2WzyhkJFcoqUNJDtQIm2ZKMk6OW8JtooG4JDxlFVc5EJXLqSwWkN5MuBpoeX2AGTNmWSghICpBUvieHHA44HMhuDmQdKIldXoq3tlv9Fu9rzh32Ur6uufeMQhzU24cPVkRx1Yt1OpUM7OXUw7nmRa6yybbkAJJhjXNwONBlOJCVoCTupQKTIvlHmlxHXJVjnm/b9yiVy9bKnWWQeZE2JAeQUrnjfHY40HU4kLWgJBamApNt5KYfABZtjOP95RGs2RRHBT1lR/bzcade+XCkSEg0xBmVLcsdI7tOV3Fq6nBg93Agq0FJLEgFJjlVTmRCOLfigEQ0dBsfegJVmE41T3wZxnefrDW7qpgVUceGZPHJOToc6EIcyHpQEi+1LK1tkuVgeOlhuTh7vwAqij3IZX6kRq7CLdoYxaPMtf3k7CZjQwoxEtvJh9SFeqFTVYcDKRzoEqCk+sqRr4ihIfIWtUk+J6k+KL0Ya7SO3++K45ZdtvPucMDhwJ7hQJcBJZs92g0iQKeOBkpIctTS6loOvcqaGQQU2UMOhnbdnHeHAw4Hdp0D9CvtWhT2llq7QTAvNLPgmvCUqI9xYVa8bdd6GKe2DgccDmzDgS4nKW3zBM4JhwMOB/YqDlDWcMjhgMMBhwPZwwEHlLKnLZyaOBxwOEAOOKDkdAOHAw4HsooDDihlVXM4lXE44HDAASWnDzgccDiQVRxwQCmrmsOpjMMBhwMOKDl9wOGAw4Gs4sD/A+QI/vOwZ2BeAAAAAElFTkSuQmCC")
header_html = "<img src='data:image/png;base64,{}' class='img-fluid'>".format(logo)
st.markdown(
header_html, unsafe_allow_html=True,
)
#Custom button color to bring prominence to executable actions
m = st.markdown("""
<style>
div.stButton > button:first-child {
background-color: #ff0000;
color:#ffffff;
}
div.stButton > button:hover {
background-color: #8b0000;
color:#ff0000;
}
</style>""", unsafe_allow_html=True)
#This removes Streamlit default settings icons
hide_streamlit_style = """
<style>
#MainMenu {visibility: visible;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
# Top-level Kepler export button removed. Use the Kepler download button shown below the embedded map.
### Global Variables ###
get_headings = ""
selected_encoding = ""
icon_options = ["Yellow Paddle", "Green Paddle", "Blue Paddle", "White Paddle", "Teal Paddle", "Red Paddle", "Yellow Pushpin", "White Pushpin", "Red Pushpin", "Square"]
selected_icon = {'Square' :'http://maps.google.com/mapfiles/kml/shapes/placemark_square.png','Yellow Pushpin' : "http://maps.google.com/mapfiles/kml/pushpin/ylw-pushpin.png",'Red Pushpin' : "http://maps.google.com/mapfiles/kml/pushpin/red-pushpin.png",'White Pushpin' : "http://maps.google.com/mapfiles/kml/pushpin/wht-pushpin.png",'Red Paddle' : "http://maps.google.com/mapfiles/kml/paddle/red-circle.png",'Green Paddle' : "http://maps.google.com/mapfiles/kml/paddle/grn-circle.png",'Blue Paddle' : "http://maps.google.com/mapfiles/kml/paddle/blu-circle.png",'Teal Paddle' : "http://maps.google.com/mapfiles/kml/paddle/ltblu-circle.png",'Yellow Paddle' : "http://maps.google.com/mapfiles/kml/paddle/ylw-circle.png",'White Paddle' : "http://maps.google.com/mapfiles/kml/paddle/wht-circle.png"}
invalid_ips = ['0', '10.', '127.0.0.1','172.16', '172.17', '172.18', '172.19', '172.2', '172.21', '172.22', '172.23', '172.24', '172.25',
'172.26', '172.27', '172.28', '172.29', '172.30', '172.31', '192.168', '169.254', "255.255" ,"fc00"]
geo_list = []
# ---------------- Hotspot / Clustering Helpers ----------------
def compute_hotspots(df: pandas.DataFrame, radius_m: float, min_samples: int, time_col: Optional[str], trim_chaining: bool = True):
"""Run DBSCAN (haversine) on LATITUDE/LONGITUDE columns (meters radius) and return
(clusters_df, summary_df).
Notes
-----
DBSCAN's notion of a cluster allows *chaining*: points can be connected via a series
of <= eps links even if the overall diameter is >> eps. That can yield MAX_DISTANCE_M
much larger than the user-selected radius. When trim_chaining is True we post-filter
each cluster to retain only points within radius_m of the cluster centroid; any points
outside are re-labelled as noise. Clusters falling below min_samples after trimming
are discarded. This makes MAX_DISTANCE_M always <= radius_m (or very close due to
floating error) and matches an intuitive "circular hotspot" expectation.
"""
try:
from sklearn.cluster import DBSCAN # dynamic import in case installed after first run
except Exception as e:
raise RuntimeError("scikit-learn not available: install scikit-learn") from e
earth_radius_m = 6371000.0
eps = radius_m / earth_radius_m
coords_rad = np.radians(df[['LATITUDE','LONGITUDE']].to_numpy())
model = DBSCAN(eps=eps, min_samples=min_samples, metric='haversine')
labels = model.fit_predict(coords_rad)
df = df.copy()
df['HOTSPOT_ID'] = labels
clusters = df[df['HOTSPOT_ID'] != -1].copy()
if clusters.empty:
return df, pandas.DataFrame(columns=['HOTSPOT_ID','COUNT','CENTER_LAT','CENTER_LON','MAX_DISTANCE_M','RADIUS_INPUT_M','FIRST_OBS','LAST_OBS'])
earth_r = earth_radius_m
summary_rows = []
groupby = clusters.groupby('HOTSPOT_ID')
# We may need to relabel after trimming; collect relabel operations
relabel_noise_indices: list[int] = []
for cid, grp in groupby:
lat_mean = grp['LATITUDE'].mean(); lon_mean = grp['LONGITUDE'].mean()
lat_mean_r, lon_mean_r = np.radians(lat_mean), np.radians(lon_mean)
lat_vals = grp['LATITUDE'].to_numpy(); lon_vals = grp['LONGITUDE'].to_numpy()
lat_r = np.radians(lat_vals); lon_r = np.radians(lon_vals)
dlat = lat_r - lat_mean_r; dlon = lon_r - lon_mean_r
a = np.sin(dlat/2)**2 + np.cos(lat_mean_r) * np.cos(lat_r) * np.sin(dlon/2)**2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))
dists = earth_r * c # meters from centroid
if trim_chaining:
keep_mask = dists <= radius_m * 1.0005 # small tolerance
if not np.all(keep_mask):
# mark dropped points (by original index) to become noise
dropped = grp.loc[~keep_mask]
relabel_noise_indices.extend(dropped.index.tolist())
grp = grp.loc[keep_mask]
dists = dists[keep_mask]
# After optional trimming, maybe cluster too small
if len(grp) < min_samples:
# whole cluster becomes noise
relabel_noise_indices.extend(grp.index.tolist())
continue
max_dist = float(dists.max()) if len(dists) else 0.0
first_time = last_time = None
if time_col and time_col in grp.columns:
times = pandas.to_datetime(grp[time_col], errors='coerce').dropna()
if not times.empty:
first_time, last_time = times.min(), times.max()
summary_rows.append({
'HOTSPOT_ID': cid,
'COUNT': len(grp),
'CENTER_LAT': lat_mean,
'CENTER_LON': lon_mean,
'MAX_DISTANCE_M': round(max_dist,2),
'RADIUS_INPUT_M': radius_m,
'FIRST_OBS': first_time,
'LAST_OBS': last_time
})
# Apply relabeling (set to noise)
if relabel_noise_indices:
df.loc[relabel_noise_indices, 'HOTSPOT_ID'] = -1
clusters = df[df['HOTSPOT_ID'] != -1].copy()
# Rebuild summary_df if we trimmed any clusters away entirely
if relabel_noise_indices:
# regenerate summary from summary_rows already filtered
pass
summary_df = pandas.DataFrame(summary_rows).sort_values(by='COUNT', ascending=False).reset_index(drop=True)
return df, summary_df
# Cached wrapper so repeated UI reruns don't recompute unnecessarily
@st.cache_data(show_spinner=False)
def cached_compute_hotspots(df: pandas.DataFrame, radius_m: float, min_samples: int, time_col: Optional[str], trim_chaining: bool = True):
return compute_hotspots(df, radius_m, min_samples, time_col, trim_chaining=trim_chaining)
def render_tactical_clock(points_df: pandas.DataFrame, time_col: str, title: str = "Tactical Clock", height: int = 520,
center_lat: Optional[float] = None, center_lon: Optional[float] = None, radius_m: Optional[float] = None,
visits: Optional[int] = None, max_distance_m: Optional[float] = None,
first_obs: Optional[pandas.Timestamp] = None, last_obs: Optional[pandas.Timestamp] = None):
"""Polar day/time chart with equal day wedges and radial hours.
- Angular: 7 equal wedges (Mon..Sun clockwise)
- Radial: hour (0 center -> 24 outer)
- Color: count of observations for (day, hour)
"""
if time_col not in points_df.columns:
return
times = pandas.to_datetime(points_df[time_col], errors='coerce').dropna()
if times.empty:
return
day_idx = times.dt.dayofweek.to_numpy() # 0=Mon
hours = times.dt.hour.to_numpy()
counts = np.zeros((7,24), dtype=int)
for d, h in zip(day_idx, hours):
counts[d, h] += 1
max_count = counts.max()
if max_count == 0:
return
try:
import plotly.graph_objects as go
except Exception:
st.warning("Plotly not installed; tactical clock unavailable.")
return
day_names = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']
day_wedge = 360/7
thetas = []
rs = []
bases = []
widths = []
colors = []
texts = []
for d in range(7):
theta_center = d*day_wedge + day_wedge/2
for h in range(24):
c = counts[d, h]
thetas.append(theta_center)
bases.append(h)
rs.append(1) # 1 hour thickness
widths.append(day_wedge * 0.90) # a touch more gap to reduce visual crowding
colors.append(c)
texts.append(f"Day: {day_names[d]}<br>Hour: {h:02d}:00<br>Count: {c}")
fig = go.Figure()
fig.add_trace(go.Barpolar(
theta=thetas,
r=rs,
base=bases,
width=widths,
marker=dict(
color=colors,
colorscale='Viridis',
cmin=0,
cmax=max_count if max_count>0 else 1,
line=dict(color='#222', width=0.3),
colorbar=dict(
title='Count',
orientation='h',
x=0.5,
y=-0.18,
xanchor='center',
yanchor='top',
len=0.55,
thickness=12
)
),
hovertemplate="%{text}<extra></extra>",
text=texts
))
day_tick_vals = [d*day_wedge + day_wedge/2 for d in range(7)]
radial_ticks = list(range(0,25,3))
# Build info block (embedded into title for PNG export completeness)
info_lines = []
if center_lat is not None and center_lon is not None:
info_lines.append(f"Center {center_lat:.5f}, {center_lon:.5f}")
if radius_m is not None:
info_lines.append(f"Radius {int(radius_m)}m")
if visits is not None:
info_lines.append(f"Visits {visits}")
if max_distance_m is not None:
info_lines.append(f"MaxDist {max_distance_m}m")
# Time window
if first_obs is not None and last_obs is not None:
try:
fstr = pandas.to_datetime(first_obs).strftime('%Y-%m-%d %H:%M')
lstr = pandas.to_datetime(last_obs).strftime('%Y-%m-%d %H:%M')
info_lines.append(f"Span {fstr} → {lstr}")
except Exception:
pass
info_html = " | ".join(info_lines)
title_html = title if not info_html else f"{title}<br><span style='font-size:12px;color:#bbb'>{info_html}</span>"
fig.update_layout(
title={'text': title_html, 'x':0.5, 'xanchor':'center'},
polar=dict(
bgcolor='#0d0d0d',
angularaxis=dict(
direction='clockwise',
rotation=90,
tickmode='array',
tickvals=day_tick_vals,
ticktext=day_names,
gridcolor='#222',
tickfont=dict(size=13, color='#ddd')
),
radialaxis=dict(
range=[0,24.8], # extend slightly to give day labels breathing room
tickmode='array',
tickvals=radial_ticks,
ticktext=[str(t) for t in radial_ticks],
tickfont=dict(size=10, color='#aaa'),
angle=0,
gridcolor='#222'
)
),
margin=dict(l=25, r=25, t=90 if info_html else 60, b=70),
template='plotly_dark',
height=height,
annotations=[]
)
# Remove unusable zoom/pan controls while retaining image download & fullscreen
remove_buttons = [
'zoom2d','pan2d','select2d','lasso2d','zoomIn2d','zoomOut2d','autoScale2d','resetScale2d'
]
st.plotly_chart(
fig,
use_container_width=True,
config={
'displaylogo': False,
'modeBarButtonsToRemove': remove_buttons,
'responsive': True
}
)
#### Functions Live Here ######
def add_color_legend(Map, df):
"""
Adds a color legend to the map for multiple data sources
"""
if 'SOURCE_FILE' in df.columns and 'POINT_COLOR' in df.columns:
# Get unique combinations of source files and colors
legend_items = df[['SOURCE_FILE', 'POINT_COLOR']].drop_duplicates()
# Create HTML for the legend
legend_html = '''
<div style="position: fixed;
bottom: 10px;
right: 10px;
z-index: 1000;
background-color: #333333;
color: white;
padding: 5px;
border-radius: 5px;
border: 2px solid grey;
">
<h5>Data Sources</h5>
'''
# Add each source file and its color to the legend
for _, row in legend_items.iterrows():
legend_html += f'''
<div style="display: flex; align-items: center; margin: 5px;">
<div style="width: 15px;
height: 15px;
background-color: {row['POINT_COLOR']};
border-radius: 50%;
margin-right: 5px;">
</div>
<span>{row['SOURCE_FILE']}</span>
</div>
'''
legend_html += '</div>'
# Add the legend to the map
Map.get_root().html.add_child(folium.Element(legend_html))
def get_bounds(feature_collection):
"""Calculate bounds from feature collection"""
lats = []
lngs = []
for feature in feature_collection['features']:
coords = feature['geometry']['coordinates']
for coord in coords:
lats.append(coord[1])
lngs.append(coord[0])
return [[min(lats), min(lngs)], [max(lats), max(lngs)]]
def hex_to_rgb(hex_color: str):
"""Convert #RRGGBB hex to [r,g,b] list of ints for kepler color ranges."""
if not isinstance(hex_color, str):
return [255, 0, 0]
h = hex_color.lstrip('#')
if len(h) != 6:
# fallback to red
return [255, 0, 0]
try:
return [int(h[i:i+2], 16) for i in (0, 2, 4)]
except Exception:
return [255, 0, 0]
def convert_to_datetime_and_string(timestamp_string): # function takes a timestamp string and converts to datetime and a uniform string output
# parses the timestamp string into a datetime object
datetime_value = parser.parse(timestamp_string)
# Formats the datetime object into the desired string format
formatted_string = datetime_value.strftime("%Y-%m-%dT%H:%M:%S")
return datetime_value, formatted_string
def get_point_at_distance(lat1, lon1, d, bearing, R=6371): # used to draw tower wedges
"""
lat: initial latitude, in degrees
lon: initial longitude, in degrees
d: target distance from initial
bearing: (true) heading in degrees
R: optional radius of sphere, defaults to mean radius of earth
Returns new lat/lon coordinate {d}km from initial, in degrees
"""
lat1 = radians(lat1)
lon1 = radians(lon1)
a = radians(bearing)
lat2 = asin(sin(lat1) * cos(d/R) + cos(lat1) * sin(d/R) * cos(a))
lon2 = lon1 + atan2(
sin(a) * sin(d/R) * cos(lat1),
cos(d/R) - sin(lat1) * sin(lat2)
)
return (degrees(lat2), degrees(lon2),)
def make_geofence_map():
# --- Geofence Manager State ---
if safe_session_get('geofences') is None:
safe_session_set('geofences', []) # list of dicts: id,name,color,geometry(type,wkt),created,updated,active,notes
if safe_session_get('geofence_counter') is None:
safe_session_set('geofence_counter', 1)
help_Box = st.expander(label="Help")
with help_Box:
st.markdown("""
Welcome to Fetch! This area is used to create geofences. To upload location data use the input area above.
- Use the draw toolbar on the map (polygon/rectangle).
- Search for a street address or get the map of an IP address. See the IP address mapping tab to carry out bulk searches.
""")
# Search / locate
with st.form("geoform"):
user_geo_input = st.text_input("Search (Address/IP)", placeholder="123 Main St or 8.8.8.8")
search = st.form_submit_button("Locate")
# Initialize session state for caching geocoding results
if safe_session_get('cached_geocode_result') is None:
safe_session_set('cached_geocode_result', None)
if safe_session_get('cached_search_term') is None:
safe_session_set('cached_search_term', None)
global geomap
# Initialize with neutral continental US view; we'll optionally recenter below
geomap = folium.Map(zoom_start=4, location=[39,-98])
Draw(export=True, draw_options={'circle': False,'circlemarker':False, 'marker':False}).add_to(geomap)
folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
attr='Esri', name='Esri Satellite', overlay=False, control=True).add_to(geomap)
folium.LayerControl(position="topright", collapsed=True).add_to(geomap)
# Geocode/IP locate - Only run when search button is clicked
if search and user_geo_input:
ipv4_ipv6_regex = "(^\s*((([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5]))\s*$)|(^\s*((([0-9A-Fa-f]{1,4}:){7}([0-9A-Fa-f]{1,4}|:))|(([0-9A-Fa-f]{1,4}:){6}(:[0-9A-Fa-f]{1,4}|((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){5}(((:[0-9A-Fa-f]{1,4}){1,2})|:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){4}(((:[0-9A-Fa-f]{1,4}){1,3})|((:[0-9A-Fa-f]{1,4})?:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){3}(((:[0-9A-Fa-f]{1,4}){1,4})|((:[0-9A-Fa-f]{1,4}){0,2}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){2}(((:[0-9A-Fa-f]{1,4}){1,5})|((:[0-9A-Fa-f]{1,4}){0,3}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){1}(((:[0-9A-Fa-f]{1,4}){1,6})|((:[0-9A-Fa-f]{1,4}){0,4}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(:(((:[0-9A-Fa-f]{1,4}){1,7})|((:[0-9A-Fa-f]{1,4}){0,5}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:)))(%.+)?\s*$)"
# Check if we need to make a new API call
# Use safe_session_get to avoid KeyError if cached_search_term is missing between reruns
if user_geo_input != safe_session_get('cached_search_term'):
if re.search(ipv4_ipv6_regex, user_geo_input):
try:
st.info("🔍 Looking up IP address... (API call)")
ip_res = geocoder.ipinfo(user_geo_input)
if ip_res and ip_res.latlng:
# Cache the successful result
safe_session_set('cached_geocode_result', {
'type': 'ip',
'data': ip_res,
'input': user_geo_input
})
safe_session_set('cached_search_term', user_geo_input)
st.success("✅ IP address located successfully!")
else:
st.error("❌ Could not locate IP address")
safe_session_set('cached_geocode_result', None)
safe_session_set('cached_search_term', None)
except Exception as e:
st.error(f"❌ IP lookup failed: {str(e)}")
safe_session_set('cached_geocode_result', None)
safe_session_set('cached_search_term', None)
else:
try:
st.info("🔍 Looking up address... (API call)")
geo_res = geocoder.arcgis(user_geo_input)
if geo_res and geo_res.latlng:
# Cache the successful result
safe_session_set('cached_geocode_result', {
'type': 'address',
'data': geo_res,
'input': user_geo_input
})
safe_session_set('cached_search_term', user_geo_input)
st.success("✅ Address located successfully!")
else:
st.error("❌ Could not locate address")
safe_session_set('cached_geocode_result', None)
safe_session_set('cached_search_term', None)
except Exception as e:
st.error(f"❌ Address lookup failed: {str(e)}")
safe_session_set('cached_geocode_result', None)
safe_session_set('cached_search_term', None)
else:
st.info("📋 Using cached result (no API call needed)")
# Display cached results if available (use .get() to avoid KeyError)
if safe_session_get('cached_geocode_result'):
cached_result = safe_session_get('cached_geocode_result')
if cached_result['type'] == 'ip':
ip_res = cached_result['data']
user_geo_input = cached_result['input']
# Format geocoder response for better readability
st.write("**IP Geolocation Results - Location represents an estimated geographic area and does not indicate the point of usage.**")
# Create organized display of key information
col1, col2 = st.columns(2)
with col1:
st.write("**Location Information:**")
location_info = {
"IP Address": getattr(ip_res, 'ip', user_geo_input),
"City": getattr(ip_res, 'city', 'Not available'),
"State/Region": getattr(ip_res, 'state', 'Not available'),
"Country": getattr(ip_res, 'country', 'Not available'),
"Postal Code": getattr(ip_res, 'postal', 'Not available'),
"Coordinates": f"{ip_res.latlng[0]:.6f}, {ip_res.latlng[1]:.6f}",
"Timezone": getattr(ip_res, 'timezone', 'Not available')
}
for key, value in location_info.items():
st.write(f"• **{key}:** {value}")
with col2:
st.write("**🌐 Network Information:**")
network_info = {
"Organization": getattr(ip_res, 'org', 'Not available'),
"Status": getattr(ip_res, 'status', 'Unknown'),
"Provider": ip_res.provider if hasattr(ip_res, 'provider') else 'ipinfo.io'
}
for key, value in network_info.items():
st.write(f"• **{key}:** {value}")
# Show raw data in an expandable section
with st.expander("🔧 Raw Geocoder Data (Debug)"):
# Show all available attributes in a more organized way
all_attrs = {}
for attr in dir(ip_res):
if not attr.startswith('_'):
try:
value = getattr(ip_res, attr)
if not callable(value):
all_attrs[attr] = value
except:
pass
st.json(all_attrs)
# Rebuild map centered on result
geomap = folium.Map(location=ip_res.latlng, zoom_start=11)
Draw(export=True, draw_options={'circle': False,'circlemarker':False, 'marker':False}).add_to(geomap)
folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
attr='Esri', name='Esri Satellite', overlay=False, control=True).add_to(geomap)
folium.LayerControl(position="topright", collapsed=True).add_to(geomap)
# Create enhanced popup with correct field names
popup_content = f"""
<div style="width: 350px; font-family: Arial, sans-serif;">
<h4 style="margin: 0 0 10px 0; color: #2c3e50;">🌐 IP Geolocation Details</h4>
<table style="width: 100%; border-collapse: collapse; font-size: 13px;">
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">IP Address:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(ip_res, 'ip', user_geo_input)}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Location:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(ip_res, 'city', 'Unknown')}, {getattr(ip_res, 'state', 'Unknown')}</td></tr>
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Country:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(ip_res, 'country', 'Unknown')}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Postal Code:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(ip_res, 'postal', 'N/A')}</td></tr>
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Coordinates:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{ip_res.latlng[0]:.6f}, {ip_res.latlng[1]:.6f}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Organization:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(ip_res, 'org', 'Unknown')}</td></tr>
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Timezone:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(ip_res, 'timezone', 'Unknown')}</td></tr>
</table>
<div style="margin-top: 8px; font-size: 11px; color: #6c757d; text-align: center;">
Data provided by {getattr(ip_res, 'provider', 'ipinfo.io')}
</div>
</div>
"""
# Add enhanced marker
folium.Marker(
location=ip_res.latlng,
tooltip=f"🌐 {getattr(ip_res, 'ip', user_geo_input)} - {getattr(ip_res, 'city', 'Unknown')}, {getattr(ip_res, 'state', 'Unknown')}",
popup=folium.Popup(popup_content, max_width=400),
icon=folium.Icon(color='blue', icon='info-sign')
).add_to(geomap)
elif cached_result['type'] == 'address':
geo_res = cached_result['data']
user_geo_input = cached_result['input']
# Format geocoder response for better readability
st.write("**🔍 Address Geolocation Results:**")
# Create organized display of key information
col1, col2 = st.columns(2)
with col1:
st.write("**📍 Location Information:**")
location_info = {
"Search Term": user_geo_input,
"Full Address": getattr(geo_res, 'address', 'Not available'),
"City": getattr(geo_res, 'city', 'Not available'),
"State": getattr(geo_res, 'state', 'Not available'),
"Country": getattr(geo_res, 'country', 'Not available'),
"Postal Code": getattr(geo_res, 'postal', 'Not available'),
"Coordinates": f"{geo_res.latlng[0]:.6f}, {geo_res.latlng[1]:.6f}"
}
for key, value in location_info.items():
st.write(f"• **{key}:** {value}")
with col2:
st.write("**🎯 Accuracy Information:**")
accuracy_info = {
"Confidence": getattr(geo_res, 'confidence', 'Not available'),
"Provider": geo_res.provider if hasattr(geo_res, 'provider') else 'ArcGIS',
"Status": getattr(geo_res, 'status', 'Unknown')
}
for key, value in accuracy_info.items():
st.write(f"• **{key}:** {value}")
# Show raw data in an expandable section
with st.expander("🔧 Raw Geocoder Data (Debug)"):
all_attrs = {}
for attr in dir(geo_res):
if not attr.startswith('_'):
try:
value = getattr(geo_res, attr)
if not callable(value):
all_attrs[attr] = value
except:
pass
st.json(all_attrs)
geomap = folium.Map(location=geo_res.latlng, zoom_start=16)
Draw(export=True, draw_options={'circle': False,'circlemarker':False, 'marker':False}).add_to(geomap)
folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
attr='Esri', name='Esri Satellite', overlay=False, control=True).add_to(geomap)
folium.LayerControl(position="topright", collapsed=True).add_to(geomap)
# Create enhanced popup for address
popup_content = f"""
<div style="width: 350px; font-family: Arial, sans-serif;">
<h4 style="margin: 0 0 10px 0; color: #2c3e50;">📍 Address Details</h4>
<table style="width: 100%; border-collapse: collapse; font-size: 13px;">
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Search Term:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{user_geo_input}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Full Address:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(geo_res, 'address', 'Not available')}</td></tr>
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">City:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(geo_res, 'city', 'Unknown')}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">State:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(geo_res, 'state', 'Unknown')}</td></tr>
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Country:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(geo_res, 'country', 'Unknown')}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Postal Code:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(geo_res, 'postal', 'N/A')}</td></tr>
<tr style="background-color: #f8f9fa;"><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Coordinates:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{geo_res.latlng[0]:.6f}, {geo_res.latlng[1]:.6f}</td></tr>
<tr><td style="font-weight: bold; padding: 5px; border: 1px solid #dee2e6;">Confidence:</td><td style="padding: 5px; border: 1px solid #dee2e6;">{getattr(geo_res, 'confidence', 'Unknown')}</td></tr>
</table>
<div style="margin-top: 8px; font-size: 11px; color: #6c757d; text-align: center;">
Data provided by {getattr(geo_res, 'provider', 'ArcGIS')}
</div>
</div>
"""
# Add enhanced marker
folium.Marker(
location=geo_res.latlng,
tooltip=f"📍 {getattr(geo_res, 'address', user_geo_input)}",
popup=folium.Popup(popup_content, max_width=400),
icon=folium.Icon(color='red', icon='map-marker')
).add_to(geomap)
# Render existing saved geofences
bounds_points = []
geofences_list = safe_session_get('geofences') or []
if geofences_list:
for g in geofences_list:
if not g.get('active'): # skip inactive
continue
try:
feature = {
'type': 'Feature',
'geometry': {'type': g['type'], 'coordinates': g['coordinates']},
'properties': {'name': g['name']}
}
folium.GeoJson(
feature,
name=g['name'],
tooltip=g['name'],
style_function=lambda feat, col=g['color']: {
'color': col,
'weight': 2,
'fillColor': col,
'fillOpacity': 0.15
}
).add_to(geomap)
# Collect bounds points for recentering
geom_type = g['type']
coords = g['coordinates']
if geom_type == 'Polygon':
for lon, lat in coords[0]:
bounds_points.append((lat, lon))
elif geom_type == 'LineString':
for lon, lat in coords:
bounds_points.append((lat, lon))
elif geom_type == 'Point':
lon, lat = coords
bounds_points.append((lat, lon))
except Exception:
continue
# Include current drawing in bounds
last_geojson_temp = None
try:
last_geojson_temp = safe_session_get('last_drawn_raw')
except Exception:
last_geojson_temp = None
# Fallback: attempt to get from current output later; we will set after outputmap if needed.
# Try to render interactive folium via streamlit_folium; if the component fails to load
# (common on deployments where the frontend component isn't available), fall back to
# embedding the map HTML. This avoids the "component failed to load" reset loop.
outputmap = None
try:
outputmap = st_folium(geomap, width=1100, height=600, key="geofence_map")
except Exception as e:
# Log the error for debugging but don't crash the app; render HTML fallback.
try:
st.warning("streamlit_folium component failed to load; falling back to static HTML rendering.")
except Exception:
pass
try:
# Render map HTML as a safe fallback. This won't provide the drawing callbacks
# that st_folium exposes, so we attempt to preserve the last drawn geometry
# from session_state instead of relying on the component.
html = geomap._repr_html_()
import streamlit.components.v1 as components
components.html(html, height=600, scrolling=True)
except Exception:
# If even HTML rendering fails, show a placeholder message.
try:
st.error("Unable to render the map via streamlit_folium or HTML fallback.")
except Exception:
pass
# Capture last drawn geometry. If we used the HTML fallback, prefer the last stored drawing
# from session_state because the fallback doesn't provide live drawing callbacks.
last_geojson = None
try:
if isinstance(outputmap, dict):
last_geojson = outputmap.get('last_active_drawing')
if not last_geojson:
# fallback to any previously stored drawing
last_geojson = safe_session_get('last_drawn_raw')
except Exception:
last_geojson = safe_session_get('last_drawn_raw')
if last_geojson:
safe_session_set('last_drawn_raw', last_geojson)
# Update bounds with current drawing
try:
g_t = last_geojson.get('geometry', {}).get('type')
g_c = last_geojson.get('geometry', {}).get('coordinates')
if g_t == 'Polygon':
for lon, lat in g_c[0]:
bounds_points.append((lat, lon))
elif g_t == 'LineString':
for lon, lat in g_c:
bounds_points.append((lat, lon))
elif g_t == 'Point':
lon, lat = g_c
bounds_points.append((lat, lon))
except Exception:
pass
# Fit bounds if we have enough points and map still default
if bounds_points:
try:
lats = [p[0] for p in bounds_points]
lons = [p[1] for p in bounds_points]
sw = (min(lats), min(lons))
ne = (max(lats), max(lons))
# Add a slight padding
pad_lat = (ne[0]-sw[0]) * 0.05 if ne[0]!=sw[0] else 0.01
pad_lon = (ne[1]-sw[1]) * 0.05 if ne[1]!=sw[1] else 0.01
geomap.fit_bounds([[sw[0]-pad_lat, sw[1]-pad_lon], [ne[0]+pad_lat, ne[1]+pad_lon]])
except Exception:
pass
st.markdown("### Current Drawing (Instant Coordinates)")
coord_list = []
g_type = None
if last_geojson:
geom_obj = last_geojson.get('geometry', {})
g_type = geom_obj.get('type')
raw_coords = geom_obj.get('coordinates')
try:
if g_type == 'Polygon' and raw_coords:
for lon, lat in raw_coords[0]:
coord_list.append((lat, lon))
elif g_type == 'LineString' and raw_coords:
for lon, lat in raw_coords:
coord_list.append((lat, lon))
elif g_type == 'Point' and raw_coords:
lon, lat = raw_coords
coord_list.append((lat, lon))
except Exception:
coord_list = []
if coord_list:
coords_text = "Latitude, Longitude\n" + "\n".join(f"{lat}, {lon}" for lat, lon in coord_list)
st.caption(f"Geometry: {g_type} | Vertices: {len(coord_list)}")
st.text_area("Coordinates", value=coords_text, height=160, key="coord_display", help="Copy / paste ready")
colx1, colx2, colx3 = st.columns(3)
with colx1:
st.download_button("Download TXT", data=coords_text, file_name="geofence_coordinates.txt")
with colx2:
# Quick GeoJSON export of just this drawing
import json as _json
feature = {
"type":"FeatureCollection",
"features":[{"type":"Feature","geometry":{"type":g_type,"coordinates":last_geojson['geometry']['coordinates']},"properties":{}}]
}
st.download_button("Download GeoJSON", data=_json.dumps(feature), file_name="geofence.geojson")
with colx3:
# KML (very simple) if polygon
if g_type == 'Polygon':
kml_coords = " ".join(f"{lon},{lat},0" for lat, lon in coord_list)
kml = f"""<?xml version='1.0' encoding='UTF-8'?>\n<kml xmlns='http://www.opengis.net/kml/2.2'>\n<Document><Placemark><Polygon><outerBoundaryIs><LinearRing><coordinates>{kml_coords}</coordinates></LinearRing></outerBoundaryIs></Polygon></Placemark></Document></kml>"""
st.download_button("Download KML", data=kml, file_name="geofence.kml")
else:
st.write(" ")
else:
st.info("Draw a shape (polygon/rectangle) to see coordinates below the map instantly.")
# Advanced save/manage block removed per user request.
# (Removed list management per user request)
# Removed planned feature caption to declutter UI.
def parse_text_for_IPs(text): #used to map ips
# Use precompiled regex objects for performance
ipv4_addresses = IPV4_REGEX.findall(text)
ipv6_addresses = IPV6_REGEX.findall(text)
ipv6_list = []
ip_list = list(set(ipv4_addresses))
for address in ipv6_addresses:
clean_ipv6 = [item for item in address if len(item) > 16]
if clean_ipv6: # checks for empty lists
ipv6_list.append(clean_ipv6)
unique_ip6_list = [str(inner_list[0]) for inner_list in ipv6_list]
unique_ip6_list = list(set(unique_ip6_list))
ip_list.extend(unique_ip6_list)
return ip_list
def get_IP_locale(invalidList, IPs):
"""Used to map IPs with better handling of API limits"""
valid_only = [address for address in IPs if not any(address.startswith(inval) for inval in invalidList)]
# Counter for successful lookups
lookup_count = 0
api_limit = 1000 # Daily limit for free tier
results = []
for ip in valid_only:
try:
data = cached_ip_lookup(ip)
if not data:
continue
# Check for rate limit hint
if isinstance(data, dict) and data.get('status_code') == 429:
st.error(f"🚫 IP lookup limit reached (Error 429). Daily free limit ~{api_limit}. Try later or consider sponsored expansion.")
break
results.append(data)
lookup_count += 1
if lookup_count % 25 == 0:
st.info(f"Processed {lookup_count} IPs...")