rchardptrsn

import ipinfo
from opencensus.ext.azure.log_exporter import AzureLogHandler

def main(host, port):
	print ('Starting honeypot!')
	# initialize Azure Monitoring logger object
	logger = startLogger()

	s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
	s.bind((host, port))

rchardptrsn

#!/usr/bin/env python
"""
Credit: https://gist.github.com/omnidan/1456674
Copyright (c) 2011, Daniel Bugl
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright

rchardptrsn

import sys
import pandas as pd
# Function to retrieve a list of Pandas DataFrames and their sizes in memory
# Call the function:
# pandas_dfs_in_memory(mydir=dir(),parent_vars=globals())

def pandas_dfs_in_memory(mydir,parent_vars):

    # get a list of all the local objects with sizes
    objects=[]

rchardptrsn

import geopandas as gpd
from sqlalchemy import create_engine

# create the sqlalchemy connection engine
db_connection_url = "postgresql://user:password@localhost:5432/gis_db"
con = create_engine(db_connection_url)

# read in the data
gdf = gpd.read_file('shapefile/holc_ad_data.shp')

rchardptrsn

CREATE TEMP TABLE fracking AS
SELECT f.name, f.operator, f.status, f.type, co.county
FROM fractracker f
JOIN co_counties co
ON ST_COVEREDBY(f.geom, co.geom);

-- query on TEMP TABLE to find count per county
SELECT county, COUNT(county) well_count 
FROM fracking
GROUP BY county

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-- calculate statistics on the totals
SELECT 
 SUM(f.well_count) n_obs, 
 COUNT(f.well_count) n_counties, 
 ROUND(AVG(f.well_count),2) mean,
 MIN(f.well_count) min,
 MAX(f.well_count) max,
 ROUND(PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY f.well_count) median,
 ROUND(STDDEV(f.well_count),2) as stdev
-- calculate group totals 

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SELECT 
 PERCENTILE_CONT(0.25) WITHIN GROUP(ORDER BY f.well_count) percentile25,
 PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY f.well_count) percentile50,
 PERCENTILE_CONT(0.75) WITHIN GROUP(ORDER BY f.well_count) percentile75,
 PERCENTILE_CONT(1.0) WITHIN GROUP(ORDER BY f.well_count) percentile100
FROM (
 SELECT COUNT(county) well_count 
 FROM fracking
 GROUP BY county
 ) f

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SELECT f.county, f.well_count, co.geom -- pgAdmin will recognize co.geom
FROM ( -- subquery to get totals by county
 SELECT county, COUNT(county) well_count 
 FROM fracking
 GROUP BY county
 ) f
LEFT JOIN co_counties co -- all from f, matching from co
ON f.county = co.county

rchardptrsn

import geopandas as gpd
# Read in the dataset from geojson file to geopandas dataframe
gdf = gpd.read_file('WFIGS_-_2022_Wildland_Fire_Perimeters_to_Date.geojson')

# print a description of the dataframe
print(gdf.info())

# Check for NaN records
# Result: geometry does not have any NaN but many other columns do
print(f'Columns with nan records: {gdf.columns[gdf.isna().any()].tolist()}')

rchardptrsn

import timeit
import sys
import json
import ee

# Authenticate to google earth engine
service_account = '[email protected]'
key_path = 'path_to_your_service_account_key.json'
credentials = ee.ServiceAccountCredentials(service_account, key_path)
ee.Initialize(credentials)