thomas-maschler

from pyspark.sql import SparkSession

spark = SparkSession\
        .builder\
        .appName("MyPySpark")\
        .getOrCreate()

sc = spark.sparkContext

my_file = sc.textFile("s3n://path/to/file")

thomas-maschler

import numpy as np
from math import pi, sqrt

def get_area(lat: float, d_lat: float, d_lng: float) -> float:
	"""
	Calculate geodesic area for lat/lng pixels using WGS 1984 as spatial reference.
	Pixel area for a fix pixel width and hight various with latitude.
	Pixels close to the equator have a larger areas. Area decreases towards the poles.
	
	Arguments:

thomas-maschler

import arcpy
import urllib
import json


def harvest_feature_service(baseURL, whereClause="", fields="*", token="", messages=None):
    """
    Return all records from a feature service as a feature set
    :param baseURL: The base URL of the feature service, including the layer ID
    :param whereClause: SQL where clause (optional)

thomas-maschler

# coding=utf-8

import arcpy

arcpy.env.workspace = r"Database Servers\WRICWLT111900_SQLEXPRESS.gds\cmr_forest_atlas (VERSION:dbo.DEFAULT)"
desc = arcpy.Describe(arcpy.env.workspace)

print desc.workspaceType

subtypes = {"9100100": "Loi",

thomas-maschler

import requests
import json

def request_token(user, password):

    d = {"username": user,
         "password": password,
         "referer":"http://www.arcgis.com",
         "f": "json"}
    

thomas-maschler

import arcpy
import numpy as np
import os
import gc
import sys

arcpy.env.overwriteOutput = True

def gf(scene, year, i):
    input_folder = r'G:\UMD_Landsat_tiles\%i' % year

thomas-maschler

import os
import urllib
import csv


url = 'https://firms.modaps.eosdis.nasa.gov/active_fire/text/Global_24h.csv'
file_path = 'path_where_you_what_to_save_file'

urllib.urlretrieve(url, file_path)