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How to use eg method in hypothesis
Best Python code snippet using hypothesis
Run299065DoubleEG_cff.py
Source:Run299065DoubleEG_cff.py 
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conditional_dump1.py
Source:conditional_dump1.py
1#!/usr/bin/env python32# -*- coding: utf-8 -*-3"""4Created on Mon Sep 7 09:32:53 20205scripts for dumping VTD assignments to a file when certain conditions are met6eg_gt... dumps to a file when the efficiency gap > hi_eg. This dumps out a map that's apportioned favorably7for dems'8eg_zero dumps to a file when the abs value of efficiency gap < zero_eg, that is, a fair map9eg_lt dumps to a file when efficiency gap < lo_eg, that is favorable to republicans10 11eg_gt_x does the same thing but for partitions returned by chain_xtended, which has a little diff structure12@author: dpg13"""14from gerrychain.metrics import mean_median, efficiency_gap15import district_list as dl16def eg_gt_x(part,hi_eg, state, my_apportionment,my_electionproxy, i1):17 if efficiency_gap(part.state[my_electionproxy]) > hi_eg:18 eg_val = round(efficiency_gap(part.state[my_electionproxy]),3)19 fname = 'redist_data/example_districts/' + state + '_' + my_apportionment + '_' + \20 my_electionproxy +'_gt_' + str(i1)+ '_' + str(eg_val)21 dl.part_dump(part.state, fname) #dump out the district assignment data + GEOID10 tags etc. to file22 return23 24 25 26 27def eg_zero_x(part,zero_eg, state, my_apportionment,my_electionproxy, i1):28 if abs(efficiency_gap(part.state[my_electionproxy])) < zero_eg:29 eg_val = round(efficiency_gap(part.state[my_electionproxy]),3)30 fname = 'redist_data/example_districts/' + state + '_' + my_apportionment + '_' + \31 my_electionproxy +'_eq_' + str(i1)+ '_' + str(eg_val)32 dl.part_dump(part.state, fname) #dump out the district assignment data + GEOID10 tags etc. to file33 return34 35 36def eg_lt_x(part,lo_eg, state, my_apportionment, my_electionproxy,i1):37 if efficiency_gap(part.state[my_electionproxy]) < lo_eg:38 eg_val = round(efficiency_gap(part.state[my_electionproxy]),3)39 fname = 'redist_data/example_districts/' + state + '_' + my_apportionment + '_' + \40 my_electionproxy +'_lt_' + str(i1)+ '_' + str(eg_val)41 dl.part_dump(part.state, fname) #dump out the district assignment data + GEOID10 tags etc. to file42 return43 44def eg_gt(part,hi_eg, state, my_apportionment,my_electionproxy, i1):45 if efficiency_gap(part[my_electionproxy]) > hi_eg:46 eg_val = round(efficiency_gap(part[my_electionproxy]),3)47 fname = 'redist_data/example_districts/' + state + '_' + my_apportionment + '_' + \48 my_electionproxy +'_gt_' + str(i1)+ '_' + str(eg_val)49 dl.part_dump(part, fname) #dump out the district assignment data + GEOID10 tags etc. to file50 return51 52 53 54 55def eg_zero(part,zero_eg, state, my_apportionment,my_electionproxy, i1):56 if abs(efficiency_gap(part[my_electionproxy])) < zero_eg:57 eg_val = round(efficiency_gap(part[my_electionproxy]),3)58 fname = 'redist_data/example_districts/' + state + '_' + my_apportionment + '_' + \59 my_electionproxy +'_eq_' + str(i1)+ '_' + str(eg_val)60 dl.part_dump(part, fname) #dump out the district assignment data + GEOID10 tags etc. to file61 return62 63 64def eg_lt(part,lo_eg, state, my_apportionment, my_electionproxy,i1):65 if efficiency_gap(part[my_electionproxy]) < lo_eg:66 eg_val = round(efficiency_gap(part[my_electionproxy]),3)67 fname = 'redist_data/example_districts/' + state + '_' + my_apportionment + '_' + \68 my_electionproxy +'_lt_' + str(i1)+ '_' + str(eg_val)69 dl.part_dump(part, fname) #dump out the district assignment data + GEOID10 tags etc. to file70 return...
airspace_particularities.py
Source:airspace_particularities.py
1#NAS airports origin/destination2dict_airport_nas_icao ={"GMMH":"GC", "GMML":"GC", "GMMA":"GC","LXGB":"LE","GABS":"GO","GAKA":"GO","GAKD":"GO",3 "GANK":"GO","GANR":"GO","GASO":"GO","GAYE":"GO","DIDL":"GO","DIGA":"GO","DIGL":"GO",4 "DIKO":"GO","DIMN":"GO","DIOD":"GO","DISG":"GO","DISP":"GO","DISS":"GO","DITB":"GO",5 "DITM":"GO","DIYO":"GO","DIBI":"GO","DIBK":"GO","DIAP":"GO","GAGM":"DR","GAGO":"DR",6 "GAMB":"DR","GATB":"DR","EGJJ":"LF","EGJA":"LF","EGJB":"LF","ENVH":"EG","ENLE":"EG",7 "ENXK":"EG","ENWV":"EG","ENXA":"EG","ENXB":"EG","ENXC":"EG","ENXD":"EG","ENXE":"EG",8 "ENXF":"EG","ENXG":"EG","ENXH":"EG","ENXI":"EG","ENXJ":"EG","ENXK":"EG","ENXL":"EG",9 "ENXM":"EG","ENXR":"EG","ENXS":"EG","ENXT":"EG","ENXV":"EG","ENXZ":"EG","ENSL":"EG",10 "EHFE":"EG","ENWG":"EG","EHFD":"EG","EHFZ":"EG","EHJA":"EG","EHJM":"EG","EHAK":"EG",11 "EKAR":"EG","EKSI":"EG","ENDP":"EG","ENLA":"EG","ENXP":"EG","GEML":"GM","BKPR":"LY",12 "VHHH":"Z","FHAW":"S","LICD":"LM","WSSS":"V","WMKK":"V","WIII":"V","WMSA":"V",13 "WBSB":"V","EKFA":"BI","EKKU":"BI","EKKV":"BI","EKMS":"BI","EKSR":"BI","EKSY":"BI",14 "EKTB":"BI","EKSO":"BI","EKVG":"BI","EKRN":"ES","GEHM":"GM", "EHJF":"EG", "EHKF":"EG",15 "EHDT":"EG","EHFB":"EG","GECE":"LE","OASN":"UT"}16dict_airport_nas_2_letter = {"DB":"DG","DX":"DG","ET":"ED","GQ":"GO","GG":"GO","GB":"GO","GF":"GL","GU":"GL",17"EL":"EB","TX":"K","DF":"DR","HD":"HA","UA":"UA","PA":"K","OI":"OI","OJ":"OJ","OL":"OL","OM":"OM","OR":"OR","OS":"OS",18"UB":"UB","UC":"UC","UD":"UD","UG":"UG","UK":"UK","UM":"UM","UT":"UT"}19dict_airport_nas_1_letter = {"R":"Z","F":"F","O":"O","U":"U","Z":"Z","C":"C","K":"K","M":"M","N":"N",20 "S":"S","T":"T","V":"V","Y":"Y"}21ECAC_countries = ['LO','EB','LB','LD','LC','LK','EK','EE','EF','LF','LG','LH','EI','LI',22 'EV','EY','EL','LM','EH','EP','LP','LR','LZ','LJ','LE','ES','EG','LA',23 'LQ','LW','LU','EN','LS','LT','UB','BI','ED','ET','ET','UK','LF','LI',24 'LY','LY','GC','UD','GE']25def get_nas_airport(airport_icao):26 return dict_airport_nas_icao.get(airport_icao,27 dict_airport_nas_2_letter.get(airport_icao[:2],28 dict_airport_nas_1_letter.get(airport_icao[:1],29 airport_icao[:2])))30def is_ECAC(icao):31 if len(icao)==4:32 icao = get_nas_airport(icao)33 return icao in ECAC_countries34def is_ATFM_AREA(icao):35 additional_countries = ['GM', 'DA', 'DT', 'HE', 'LL', 'OL', 'UM']36 if icao in ['UMKK']:37 pouet = False38 else:39 if len(icao)==4:40 icao = get_nas_airport(icao)41 pouet = (is_ECAC(icao) or icao in additional_countries) and not icao in ['UB'] 42 ...
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