Ryan Cooper maptastik

On Ubuntu-flavored Linux distros, there appears to be a bug where when trying to access a URL, the browser gets hung up with a Resolving host... message for a long time. Sometimes the request will just stop and then maybe after a little while longer, the page will load. This seems to have been documented going back a couple major versions of Ubuntu and while there are several workarounds, the one that I found here worked for me:

$ sudo ls -la /etc/resolv.conf
> lrwxrwxrwx 1 root root 29 mar 7 20:20 /etc/resolv.conf -> ../run/resolvconf/stub-resolv.conf
$ sudo rm -f /etc/resolv.conf
$ sudo ln -s /run/systemd/resolve/resolv.conf /etc/resolv.conf
$ sudo ls -la /etc/resolv.conf
lrwxrwxrwx 1 root root 32 mar 8 07:30 /etc/resolv.conf -> /run/systemd/resolve/resolv.conf

	import arcpy

	# Assume a feature class called polygon_fc with two separate float/double fields, x and y

	with arcpy.da.UpdateCursor('polygon_fc', ['SHAPE@', 'x', 'y']) as cursor:
	for row in cursor:
	geom_4326 = row[0].projectAs(arcpy.SpatialReference(4326))
	row[1] = geom_4326.centroid.X
	row[2] = geom_4326.centroid.Y
	cursor.updateRow(row)

	import arcpy
	import csv

	with open('output.csv', 'w', newline = '') as csvfile:
	writer = csv.writer(csvfile) # Create writer object
	writer.writerow(['Column 1', 'Column 2', 'Column 3', 'Column 4']) # Define the header row
	with arcpy.da.SearchCursor('input_layer', ['Field1', 'Field2', 'Field3', 'Field4']) as cursor: # Define cursor object
	for row in cursor:
	writer.writerow([row[0], row[1], row[2], row[3]]) # Write values from each item in cursor to csv

	csvs-to-sqlite tbl.csv tbl.db && \
	curl "<URL to enpoint and/or query that returns GeoJSON>" \| \
	ogr2ogr -f geojson -nln input /vsistdout/ /vsistdin/ \| \
	ogr2ogr -f geojson \
	-dialect sqlite \
	-sql "select input., tbl. from inputjoin 'tbl.db'.tbl AS tbl on input.<primary key> = tbl.<foreign key>" \
	output.geojson /vsistdin/ && \
	del tbl.db

	import pandas as pd
	df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4], 'c':['dd','ee','ff'], 'd':[5,9,1]})

	df['e'] = df[['a', 'b', 'd']].sum(axis = 1)

	#########################
	## RESULT ##
	#########################
	# a b c d e # COLUMNS
	# 0 1 2 dd 5 8 # ROW

	def quantile_bins(quantiles, min = 0, max = 100):
	"""Return a list of quantiles including floor and ceiling values

	Args:
	quantile (list): Quantile values in order
	min (numeric): Floor value of data range
	max (numeric): Ceiling value of data range

	Returns:
	list: An inclusive list of quantile bin values

	def quantile_percentiles(quantile_count: int = 5) -> list:
	"""Return quantile percentile values for a given number of quantiles.

	Args:
	quantile_count (int): Number of quantiles to generate percentile values for (default 5)

	Returns:
	list: Equally spaced quantile percentile values based on quantile_count

	Examples: