My résumé as a plot of a mathematical formula
All the pixel art you see below has been made by plotting Tupper's self-referential formula using matplotlib. You can find more about the process I used here.
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from numpy import vectorize
from matplotlib.colors import LinearSegmentedColormap
def plot(k):
@vectorize
def tupper(x,y):
return (y // 17 // 2 ** (17 * int(x) + int(y) % 17)) % 2 > 0.5
custom_cmap = LinearSegmentedColormap.from_list('taxi_driver', ['gold','black'])
X, Y = np.meshgrid(range(106), range(k, k + 17))
Z = np.fliplr(tupper(X, Y))
plt.figure(figsize = (106,17))
plt.imshow(Z, interpolation='nearest', cmap = custom_cmap)
plt.show()
k_basic_info_header = 916907656634872601424155456420431718387505356230636731816776599864288820722127296954422398372182884504890480778000876672401056320086167598999161692464305694554547108403557919030014879733854367033817395439983808030976133054535095608312807547167749938715816880712357464742782236030620574432698796987899961448299804745080339975639492698548383308526750368365294223077085967984169331415730548700484501623841684195319438376960
k_basic_info = 4781369434987272241034692076580216618555470146049311213980821568752340827385964551505280839746861149061698751936321645639553199361475248322431725642841499277043535120833582165426768269464501375221874460349938142199016684732305296711973449502514951372545085117593622842760947228422297326444498434090574610380729430743890413493805256807212925031197250927314895991610855151360404443348069148869468933024561194459299082236105413239655920545857578226591658533004331702697147567603570500574925575648611299379762479711953059647915955401050414831894528
k_education_header = 916907656634872601422708550875740122414678015317103115746028886887948612071591909826456218237469204490304194601470512218842509048777737253016274008716762745065413981501678023874186053133768209963055424235672385101099748141755204995266438550073061069175454745595098605478700034868551893471086550805291217976314161213908113512921186089087058301736618140956701682759708455896237196903916056008235146345760363251485804331008
k_education = 4781370616007728772172549848133290540258714489676125789458064450198918501139726024163812912075095742922008046052651672133860438923299984903482512623970202192751414463057259177822561174033381256273291109056784754455748295321442111010125037461889710425599387225215381966149091379165182253590856992705579453694140994608323381227041611882522120460146334191353173570658473011383749779454285650705456237131755947774260749029803557143746127714455971278991011097814747160082897976932095021608139762030431391478162554510802982066697837787853496239783936
k_work_header = 7033100379878673667993863431151450290720685154603486613559413498410702434616540532918275246918007079213938435551766900258187339341844143171323569481513167275257419137800271739524111025015367235740108303305334514903107548452192828577668107273530325415894099362380626552560735068073112248624701860301674915144675482332085404827648
k_work = 4781375032229698751421832485510771650998449419483833485986044745234558803317014512056417494947187010209756752990130574008760496025281594280047539759850459872499806748349913250802036970757550094132615358725580094425231672940730074047682292434746904322784597960853978959395750780031446796234951000822906543744241363684732036091875438255722652409136390861686693980600784279418972957958623952756682464146053929883487448793115703486097017600121033051398349861319169292911984095939779938473874850993514483327973682087316580048920707232711377684660462
k_skills_header = 22551147484884182963018405977805254924096115503191680090832285053815517197742709840581197283424071694063370721600636880374713559132306940520509392098764816801916846754854072606693245281489534211172678944691791937629878193590287163632617816893245934352349497239800633850363687589589031656061178114805658293518595084797965739957998649756611059768897157693536773344609639071744
k_programming_langs = 597527688270051910123779822482479170565505069675221133683889176728805793949862879671961035176866819157900223758152885580452158814244079286649031281631644393229898716899324606721148975919260341481587171791577563523881491874223506289564807782799314766968895355546950230322513369729470566220431969085565247037676834021865058310028813876823212156687505808881275347743440786384310037284125960506515813761503560497069240898796029335984978208618472812237374319287343518824434586141540595234466683929730113846746939002257287013739794534383893066285056
k_tools = 284975939276374832480243332801731880664989988380324401678887042460483354392915312590114714172319003798047999433993939938939797791046309936442463221961883285154449672878811190707636446815583363934907946785726470813512994569514836304210315381411401526864803326912606862553027983702333395698045551300406856072729845117053881097829103502955796389109124654606892792471285615101561458411332858201109603964891496755221219702611093606440790838627266411525577660375357129521351012652733761109134559929895564577320781138864892859668679580452473521831936
plot(k_basic_info_header)
plot(k_basic_info)
plot(k_education_header)
plot(k_education)
plot(k_work_header)
plot(k_work)
plot(k_skills_header)
plot(k_programming_langs)
plot(k_tools)