working now (7bfe1aa5) · Commits · Jonathan Mattingly / Atlas Examples

pycache/helper_functions.cpython-311.pyc

+3.3 KiB (7.98 KiB)

File changed.

No diff preview for this file type.

example_atlas_multilevel_precinctHistogram.py

+1 −4

Original line number	Diff line number	Diff line
		@@ -23,7 +23,6 @@ pctToDistVotes = {}
		for node in pctData["nodes"]:
		pctToDistVotes[node["id"]] = {}

		count=0

		while map is not None: # This loops through all of the map in the atlas
		try:
		@@ -46,9 +45,7 @@ while map is not None: # This loops through all of the map in the atlas
		votes["Total"] = distVoteT[node_to_dist[id]]
		pctToDistVotes[id][map.name] = votes

		count+=1
		if count>500:
		break


		except Exception:
		break

example_atlas_multilevel_precinctOutcomes.py

+18 −15

Original line number	Diff line number	Diff line
		@@ -18,15 +18,15 @@ pctData = json.load(pctDataFile)
		dataElection = pctData['nodes']
		hf.addTotalVotes(dataElection)

		#atlas = Atlas.openAtlas("./truncated_nc_multiscale.jsonl")
		atlas = Atlas.openAtlas("./atlas_measureID12.jsonl.gz")
		atlas = Atlas.openAtlas("./truncated_nc_multiscale.jsonl")
		#atlas = Atlas.openAtlas("./atlas_measureID12.jsonl.gz")
		print(atlas)
		map = []
		print("\n")

		swingElections=[]
		swings=[.46+i/200 for i in range(4*2+1)]
		electons=["G20_USS","G20_LG","G20_GV","G20_PR","G16_PR"]
		swings=[.46+i/200 for i in range(8*2+1)]
		electons=["G20_USS","G20_PR","G16_PR","G08_AG"]#,"G20_LG","G20_GV","G20_PR","G16_PR"]
		for election in electons:
		hf.addUniformSwings(swings,electons,dataElection)
		for s in swings:
		@@ -49,26 +49,29 @@ while map is not None: # This loops through all of the map in the atlas
		node_to_dist = hf.get_node_to_district(map.districting,
		pctData["nodes"])

		numDemsPrecinct=np.zeros(len(node_to_dist.keys()))
		for e in swingElections:
		distVoteR, distVoteD, distVoteT = hf.sumElection(e, node_to_dist,
		dataElection)
		for id in node_to_dist.keys():
		if distVoteD[node_to_dist[id]] >distVoteR[node_to_dist[id]]:
		numDemsPrecinct[id]+=1
		numDemsPrecinct=hf.demWinByPrecicts(swingElections,node_to_dist,dataElection)

		for id in node_to_dist.keys():
		if (numDemsPrecinct[id] in pctDemWinnersHist[id].keys()):
		pctDemWinnersHist[id][numDemsPrecinct[id]]+=1
		else:
		pctDemWinnersHist[id][numDemsPrecinct[id]]=1

		# id = 100
		# if (numDemsPrecinct[id] in pctDemWinnersHist[id].keys()):
		# pctDemWinnersHist[id][numDemsPrecinct[id]]+=1
		# print(id, "dem winner", numDemsPrecinct[id], pctDemWinnersHist[id][numDemsPrecinct[id]])
		# else:
		# pctDemWinnersHist[id][numDemsPrecinct[id]]=1
		# print(id, "dem winner", numDemsPrecinct[id], pctDemWinnersHist[id][numDemsPrecinct[id]])

		if (count % 50) == 0:
		print(count," ; ",map.name)
		print(f"{count:,}"," ; ",map.name)
		count+=1
		if count>500:
		break
		# if count>10000:
		# break
		except Exception:
		break



		# i=1200;plt.bar(pctDemWinnersHist[i].keys(),pctDemWinnersHist[i].values());plt.show()
		No newline at end of file

helper_functions.py

+70 −1

Original line number	Diff line number	Diff line
		import math

		def get_node_to_district(districting, nodes):
		# This function reconstucts for a multiscale districting description
		# the mapping from precinct IDs to Districts for a given map
		@@ -63,3 +65,70 @@ def addTotalVotes(dataElection):
		for p in dataElection:
		p[e+"_T"]=p[e+"_D"]+p[e+"_R"]

		import numpy as np

		def demWinByPrecicts(elections,node_to_dist,dataElection):
		numDemsPrecinct=np.zeros(len(node_to_dist.keys()))
		for e in elections:
		distVoteR, distVoteD, distVoteT = sumElection(e, node_to_dist,
		dataElection)
		for id in node_to_dist.keys():
		# id = 100
		# print(e, id, count, distVoteD[node_to_dist[id]], distVoteR[node_to_dist[id]])
		if distVoteD[node_to_dist[id]] >distVoteR[node_to_dist[id]]:
		numDemsPrecinct[id]+=1
		return numDemsPrecinct

		def histMode(hist):
		return max(hist, key=hist.get)

		def histNormalize(hist):
		total=sum(hist.values())
		histNorm={}
		for k in hist.keys():
		histNorm[k]=hist[k]/total
		return histNorm

		def histMean(hist):
		mn=0.0
		total=sum(hist.values())
		for k in hist.keys():
		mn+=k*hist[k]
		mn=mn/total
		return mn

		def histStd(hist,scale=1.0):
		mn=histMean(hist)
		std=0.0
		total=sum(hist.values())
		for k in hist.keys():
		std+=((k-mn)*2)hist[k]
		std=std/total
		return math.sqrt(std)/scale

		def histMedean(hist):
		totalMass=sum(hist.values())
		keys=(list(hist.keys()))
		keys.sort()
		mass=0.0
		lastMass=0.0
		kLast=0.0
		for k in keys:
		mass+=hist[k]
		if mass >= totalMass/2.0:
		break
		kLast=k
		lastMass=mass
		if kLast>0.0:
		d=k-kLast
		a=(totalMass/2.0-lastMass)/(mass-lastMass)
		return (akLast + (1.0-a)k)
		else:
		d=k
		a=(totalMass/2.0)/mass
		return (1.0-a)*k

		def histSpread(hist,scale=1.0):
		l=list(hist.keys())
		return (max(l)-min(l))/scale