def add_diversity_data_to_serovar(serovar_data):
for serovar in serovar_data:
human_removed_taxa = {}
data = serovar_data[serovar]
for taxon in data['associated_taxa']:
if taxon == 'h**o sapien':
continue
human_removed_taxa[taxon] = data['associated_taxa'][taxon]
taxa_counts = list(human_removed_taxa.values())
if len(taxa_counts) > 0 and sum(taxa_counts) >= 10:
serovar_data[serovar]['taxa_entropy'] = calc_shanon_entropy(
taxa_counts)
serovar_data[serovar]['taxa_shannon_index'] = alpha.shannon(
taxa_counts)
serovar_data[serovar]['taxa_simpson_index'] = alpha.simpson(
taxa_counts)
serovar_data[serovar]['taxa_simpson_index_e'] = alpha.simpson_e(
taxa_counts)
serovar_data[serovar]['taxa_chao1'] = alpha.chao1(taxa_counts)
plasmid_counts = list(serovar_data[serovar]['plasmids'].values())
if len(plasmid_counts) > 0 and sum(plasmid_counts) >= 10:
serovar_data[serovar]['plasmid_entropy'] = calc_shanon_entropy(
plasmid_counts)
serovar_data[serovar]['plasmid_shannon_index'] = alpha.shannon(
plasmid_counts)
serovar_data[serovar]['plasmid_simpson_index'] = alpha.simpson(
plasmid_counts)
serovar_data[serovar]['plasmid_simpson_index_e'] = alpha.simpson_e(
plasmid_counts)
serovar_data[serovar]['plasmid_chao1'] = alpha.chao1(
plasmid_counts)
return serovar_data
def test_simpson_e(self):
# A totally even community should have simpson_e = 1.
self.assertEqual(simpson_e(np.array([1, 1, 1, 1, 1, 1, 1])), 1)
arr = np.array([0, 30, 25, 40, 0, 0, 5])
freq_arr = arr / arr.sum()
D = (freq_arr**2).sum()
exp = 1 / (D * 4)
obs = simpson_e(arr)
self.assertEqual(obs, exp)
# From:
# https://groups.nceas.ucsb.edu/sun/meetings/calculating-evenness-
# of-habitat-distributions
arr = np.array([500, 400, 600, 500])
D = 0.0625 + 0.04 + 0.09 + 0.0625
exp = 1 / (D * 4)
self.assertEqual(simpson_e(arr), exp)
def test_simpson_e(self):
# A totally even community should have simpson_e = 1.
self.assertEqual(simpson_e(np.array([1, 1, 1, 1, 1, 1, 1])), 1)
arr = np.array([0, 30, 25, 40, 0, 0, 5])
freq_arr = arr / arr.sum()
D = (freq_arr ** 2).sum()
exp = 1 / (D * 4)
obs = simpson_e(arr)
self.assertEqual(obs, exp)
# From:
# https://groups.nceas.ucsb.edu/sun/meetings/calculating-evenness-
# of-habitat-distributions
arr = np.array([500, 400, 600, 500])
D = 0.0625 + 0.04 + 0.09 + 0.0625
exp = 1 / (D * 4)
self.assertEqual(simpson_e(arr), exp)
def sentence_start(text):
#Count variables
ratio_dict = {
"nouns": 0,
"pronouns": 0,
"verbs": 0,
"adjectives": 0,
"adverbs": 0,
"conjunctions": 0,
"particles": 0,
"pronouns": 0,
"prepositions": 0,
"others": 0,
"simpson": 0,
"fisher": 0,
"brillouin": 0,
"berger_parker": 0
}
#Tokenize into sentences
sentences = nltk.tokenize.sent_tokenize(text)
problem_sentences = []
#Loop through sentences
for sentence in sentences:
tags = identify_speech(sentence)
ratio_dict[tags[0]] = ratio_dict[tags[0]] + 1
if tags[0] == "nouns" or tags[0] == "pronouns":
problem_sentences.append(sentence)
#Calculate diversity
simpson = simpson_e(list(ratio_dict.values())[0:7])
fisher = fisher_alpha(list(ratio_dict.values())[0:7])
brillouin = brillouin_d(list(ratio_dict.values())[0:7])
berger_parker = berger_parker_d(list(ratio_dict.values())[0:7])
#Convert to percentage
#ratio_dict = {k: "".join([str(round(v / len(sentences),4)*100),"%"]) for k, v in ratio_dict.items()}
#Update diversity metric
ratio_dict['simpson'] = simpson
ratio_dict['fisher'] = fisher
ratio_dict['brillouin'] = brillouin
ratio_dict['berger_parker'] = berger_parker
return (ratio_dict, problem_sentences)
def mercat_compute_alpha_beta_diversity(counts,bif):
abm = dict()
abm['shannon'] = skbio_alpha.shannon(counts)
abm['simpson'] = skbio_alpha.simpson(counts)
abm['simpson_e'] = skbio_alpha.simpson_e(counts)
abm['goods_coverage'] = skbio_alpha.goods_coverage(counts)
abm['fisher_alpha'] = skbio_alpha.fisher_alpha(counts)
abm['dominance'] = skbio_alpha.dominance(counts)
abm['chao1'] = skbio_alpha.chao1(counts)
abm['chao1_ci'] = skbio_alpha.chao1_ci(counts)
abm['ace'] = skbio_alpha.ace(counts)
with open(bif + "_diversity_metrics.txt", 'w') as dmptr:
for abmetric in abm:
dmptr.write(abmetric + " = " + str(abm[abmetric]) + "\n")
def main():
agents = [1, 2, 3, 4, 5, 6, 7, 8]
signals = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8']
network = group(agents)
pairs = [list(elem) for elem in network]
menLen = 3
condition = "Heterogeneity PR R"
####SIGMAS####
s1 = rand.sample([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], 8)
s2 = rand.sample([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], 8)
sigmas = {1: s1, 2: s1, 3: s1, 4: s1, 5: s2, 6: s2, 7: s2, 8: s2}
#samples = [{"b": 1.0, "x": 0.5, "m": 0.02}]
samples = [
# {'cont': 0.0, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.0, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.0, 'mut': 0.02}]
# {'cont': 0.0, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.1, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.2, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.3, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.4, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.4, 'mut': 0.02},
{
'cont': 0.0,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.1,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.2,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.3,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.4,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.5,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.6,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.7,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.8,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.9,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 1.0,
'coord': 0.5,
'mut': 0.02
}
]
# {'cont': 0.0, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.6, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.7, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.8, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.9, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.0, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.1, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.2, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.3, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.4, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.5, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.6, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.7, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.8, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.9, 'coord': 1.0, 'mut': 0.02},
# {'cont': 1.0, 'coord': 1.0, 'mut': 0.02}]
samples = [d for d in samples for _ in range(1)]
simulations = 250
statistics = {
sim: {
agent: {
sample: {
signal: [0 for round in range(1,
len(pairs) + 1)]
for signal in signals
}
for sample in range(len(samples))
}
for agent in agents
}
for sim in range(simulations)
}
for sim in range(simulations):
network = group(agents)
pairs = [list(elem) for elem in network]
s1 = rand.sample([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
8)
s2 = rand.sample([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
8)
sigmas = {1: s1, 2: s1, 3: s1, 4: s1, 5: s2, 6: s2, 7: s2, 8: s2}
for mu in range(len(samples)):
game = Match(agents, pairs, signals, sigmas, samples[mu]["cont"],
samples[mu]["coord"], samples[mu]["mut"], menLen)
game.play()
for n, round in enumerate(game.memory):
for agent, signal in round.items():
statistics[sim][agent][mu][signal][n] += 1
with open('heterogeneity_PR_R.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile,
delimiter=';',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow([
'Simulation', 'Sample', 'Agent', 'Memory', 'Generation',
'Condition', 'Content bias', 'Coordination bias', 'Mutation rate'
] + signals + ['Population signals'] + ['Entropy_population'] +
['Entropy_subpopulation_1'] +
['Entropy_subpopulation_2'] +
['Subpopulation_1 signals'] +
['Subpopulation_2 signals'] +
['Brillouin_population'] + ['Margalef_population'] +
['Simpson_population'] + ['Simpson_e_population'] +
['Richness'])
# Creando listas que contienen la produccion de cada senal: para toda la poblacion (aux) y para cada jugador (auxn)
for agent in agents:
for sim in range(simulations):
for mu in range(len(samples)):
for round in range(1, len(pairs) + 1):
aux = [
statistics[sim][agent][mu][signal][round - 1]
for signal in signals
]
aux1 = [
statistics[sim][1][mu][signal][round - 1]
for signal in signals
]
aux2 = [
statistics[sim][2][mu][signal][round - 1]
for signal in signals
]
aux3 = [
statistics[sim][3][mu][signal][round - 1]
for signal in signals
]
aux4 = [
statistics[sim][4][mu][signal][round - 1]
for signal in signals
]
aux5 = [
statistics[sim][5][mu][signal][round - 1]
for signal in signals
]
aux6 = [
statistics[sim][6][mu][signal][round - 1]
for signal in signals
]
aux7 = [
statistics[sim][7][mu][signal][round - 1]
for signal in signals
]
aux8 = [
statistics[sim][8][mu][signal][round - 1]
for signal in signals
]
# Lista que contiene los sumatorios de cada tipo de senales producidas a nivel de la poblacion global en cada muestra y ronda
summation_pop = []
# Lista que contiene los sumatorios de cada tipo de senales producidas a nivel de subpoblacion en cada muestra y ronda
summation_subpop_1 = []
summation_subpop_2 = []
# Sumando las senales de cada tipo
for i in range(len(aux1)):
# A nivel de la poblacion
summation_pop.append(aux1[i] + aux2[i] + aux3[i] +
aux4[i] + aux5[i] + aux6[i] +
aux7[i] + aux8[i])
# A nivel de las subpoblaciones
for i in range(len(aux1)):
summation_subpop_1.append(aux1[i] + aux2[i] +
aux3[i] + aux4[i])
summation_subpop_2.append(aux5[i] + aux6[i] +
aux7[i] + aux8[i])
writer.writerow([
sim + 1, mu +
1, agent, menLen, round, condition, samples[mu]
['cont'], samples[mu]['coord'], samples[mu]['mut']
] + aux + [summation_pop] + [entropy(summation_pop)] +
[entropy(summation_subpop_1)] +
[entropy(summation_subpop_2)] +
[summation_subpop_1] +
[summation_subpop_2] +
[brillouin_d(summation_pop)] +
[margalef(summation_pop)] +
[simpson(summation_pop)] +
[simpson_e(summation_pop)] +
[observed_otus(summation_pop) / 8])
def main(menLen, i_power, sigmas, samples, simulations, condition):
agents = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
#agents = list(range(1, 101))
signals = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10']
network = group(agents)
pairs = [list(elem) for elem in network]
# i_power = 1
# menLen = 3
####SIGMAS: Agents' value system####
#s1 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#s2 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#sigmas = {1: s1, 2: s1, 3: s1, 4: s1, 5: s1, 6: s2, 7: s2, 8: s2, 9:s2, 10:s2}
# samples = [
# {'cont': 1.0, 'coord': 0.5, 'conform': 1, 'confirm':0, 'mut': 0.02}]
# samples = [d for d in samples for _ in range(1)]
#simulations = 1
statistics = {
sim: {
agent: {
sample: {
signal: [0 for round in range(1,
len(pairs) + 1)]
for signal in signals
}
for sample in range(len(samples))
}
for agent in agents
}
for sim in range(simulations)
}
for sim in range(simulations):
#network = group(agents)
#pairs = [list(elem) for elem in network]
for mu in range(len(samples)):
game = Match(
agents,
pairs,
signals,
sigmas,
samples[mu]["cont"],
samples[mu]["coord"],
samples[mu]["mut"],
menLen,
i_power,
)
game.play()
for n, round in enumerate(game.memory):
for agent, signal in round.items():
statistics[sim][agent][mu][signal][n] += 1
with open('PRep_heterogeneity_R_I005.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow([
'Simulation', 'Sample', 'Agent', 'Memory', 'Generation',
'Condition', 'Inst_power', 'Content bias', 'Coordination bias',
'Mutation rate'
] + signals + ['Population signals'] + ['Entropy_population'] +
['Entropy_subpopulation_1'] +
['Entropy_subpopulation_2'] +
['Subpopulation_1 signals'] +
['Subpopulation_2 signals'] +
['Brillouin_population'] + ['Margalef_population'] +
['Simpson_population'] + ['Simpson_e_population'] +
['Richness'])
# Creando listas que contienen la produccion de cada senal: para toda la poblacion (aux) y para cada jugador (auxn)
#for agent in agents:
for sim in range(simulations):
for mu in range(len(samples)):
for round in range(1, len(pairs) + 1):
aux = [
statistics[sim][agent][mu][signal][round - 1]
for signal in signals
]
aux1 = [
statistics[sim][1][mu][signal][round - 1]
for signal in signals
]
aux2 = [
statistics[sim][2][mu][signal][round - 1]
for signal in signals
]
aux3 = [
statistics[sim][3][mu][signal][round - 1]
for signal in signals
]
aux4 = [
statistics[sim][4][mu][signal][round - 1]
for signal in signals
]
aux5 = [
statistics[sim][5][mu][signal][round - 1]
for signal in signals
]
aux6 = [
statistics[sim][6][mu][signal][round - 1]
for signal in signals
]
aux7 = [
statistics[sim][7][mu][signal][round - 1]
for signal in signals
]
aux8 = [
statistics[sim][8][mu][signal][round - 1]
for signal in signals
]
aux9 = [
statistics[sim][9][mu][signal][round - 1]
for signal in signals
]
aux10 = [
statistics[sim][10][mu][signal][round - 1]
for signal in signals
]
# aux11 = [statistics[sim][11][mu][signal][round - 1] for signal in signals]
# aux12 = [statistics[sim][12][mu][signal][round - 1] for signal in signals]
# aux13 = [statistics[sim][13][mu][signal][round - 1] for signal in signals]
# aux14 = [statistics[sim][14][mu][signal][round - 1] for signal in signals]
# aux15 = [statistics[sim][15][mu][signal][round - 1] for signal in signals]
# aux16 = [statistics[sim][16][mu][signal][round - 1] for signal in signals]
# aux17 = [statistics[sim][17][mu][signal][round - 1] for signal in signals]
# aux18 = [statistics[sim][18][mu][signal][round - 1] for signal in signals]
# aux19 = [statistics[sim][19][mu][signal][round - 1] for signal in signals]
# aux20 = [statistics[sim][20][mu][signal][round - 1] for signal in signals]
# aux21 = [statistics[sim][21][mu][signal][round - 1] for signal in signals]
# aux22 = [statistics[sim][22][mu][signal][round - 1] for signal in signals]
# aux23 = [statistics[sim][23][mu][signal][round - 1] for signal in signals]
# aux24 = [statistics[sim][24][mu][signal][round - 1] for signal in signals]
# aux25 = [statistics[sim][25][mu][signal][round - 1] for signal in signals]
# aux26 = [statistics[sim][26][mu][signal][round - 1] for signal in signals]
# aux27 = [statistics[sim][27][mu][signal][round - 1] for signal in signals]
# aux28 = [statistics[sim][28][mu][signal][round - 1] for signal in signals]
# aux29 = [statistics[sim][29][mu][signal][round - 1] for signal in signals]
# aux30 = [statistics[sim][30][mu][signal][round - 1] for signal in signals]
# aux31 = [statistics[sim][31][mu][signal][round - 1] for signal in signals]
# aux32 = [statistics[sim][32][mu][signal][round - 1] for signal in signals]
# aux33 = [statistics[sim][33][mu][signal][round - 1] for signal in signals]
# aux34 = [statistics[sim][34][mu][signal][round - 1] for signal in signals]
# aux35 = [statistics[sim][35][mu][signal][round - 1] for signal in signals]
# aux36 = [statistics[sim][36][mu][signal][round - 1] for signal in signals]
# aux37 = [statistics[sim][37][mu][signal][round - 1] for signal in signals]
# aux38 = [statistics[sim][38][mu][signal][round - 1] for signal in signals]
# aux39 = [statistics[sim][39][mu][signal][round - 1] for signal in signals]
# aux40 = [statistics[sim][40][mu][signal][round - 1] for signal in signals]
# aux41 = [statistics[sim][41][mu][signal][round - 1] for signal in signals]
# aux42 = [statistics[sim][42][mu][signal][round - 1] for signal in signals]
# aux43 = [statistics[sim][43][mu][signal][round - 1] for signal in signals]
# aux44 = [statistics[sim][44][mu][signal][round - 1] for signal in signals]
# aux45 = [statistics[sim][45][mu][signal][round - 1] for signal in signals]
# aux46 = [statistics[sim][46][mu][signal][round - 1] for signal in signals]
# aux47 = [statistics[sim][47][mu][signal][round - 1] for signal in signals]
# aux48 = [statistics[sim][48][mu][signal][round - 1] for signal in signals]
# aux49 = [statistics[sim][49][mu][signal][round - 1] for signal in signals]
# aux50 = [statistics[sim][50][mu][signal][round - 1] for signal in signals]
# aux51 = [statistics[sim][51][mu][signal][round - 1] for signal in signals]
# aux52 = [statistics[sim][52][mu][signal][round - 1] for signal in signals]
# aux53 = [statistics[sim][53][mu][signal][round - 1] for signal in signals]
# aux54 = [statistics[sim][54][mu][signal][round - 1] for signal in signals]
# aux55 = [statistics[sim][55][mu][signal][round - 1] for signal in signals]
# aux56 = [statistics[sim][56][mu][signal][round - 1] for signal in signals]
# aux57 = [statistics[sim][57][mu][signal][round - 1] for signal in signals]
# aux58 = [statistics[sim][58][mu][signal][round - 1] for signal in signals]
# aux59 = [statistics[sim][59][mu][signal][round - 1] for signal in signals]
# aux60 = [statistics[sim][50][mu][signal][round - 1] for signal in signals]
# aux61 = [statistics[sim][61][mu][signal][round - 1] for signal in signals]
# aux62 = [statistics[sim][62][mu][signal][round - 1] for signal in signals]
# aux63 = [statistics[sim][63][mu][signal][round - 1] for signal in signals]
# aux64 = [statistics[sim][64][mu][signal][round - 1] for signal in signals]
# aux65 = [statistics[sim][65][mu][signal][round - 1] for signal in signals]
# aux66 = [statistics[sim][66][mu][signal][round - 1] for signal in signals]
# aux67 = [statistics[sim][67][mu][signal][round - 1] for signal in signals]
# aux68 = [statistics[sim][68][mu][signal][round - 1] for signal in signals]
# aux69 = [statistics[sim][69][mu][signal][round - 1] for signal in signals]
# aux70 = [statistics[sim][70][mu][signal][round - 1] for signal in signals]
# aux71 = [statistics[sim][71][mu][signal][round - 1] for signal in signals]
# aux72 = [statistics[sim][72][mu][signal][round - 1] for signal in signals]
# aux73 = [statistics[sim][73][mu][signal][round - 1] for signal in signals]
# aux74 = [statistics[sim][74][mu][signal][round - 1] for signal in signals]
# aux75 = [statistics[sim][75][mu][signal][round - 1] for signal in signals]
# aux76 = [statistics[sim][76][mu][signal][round - 1] for signal in signals]
# aux77 = [statistics[sim][77][mu][signal][round - 1] for signal in signals]
# aux78 = [statistics[sim][78][mu][signal][round - 1] for signal in signals]
# aux79 = [statistics[sim][79][mu][signal][round - 1] for signal in signals]
# aux80 = [statistics[sim][80][mu][signal][round - 1] for signal in signals]
# aux81 = [statistics[sim][81][mu][signal][round - 1] for signal in signals]
# aux82 = [statistics[sim][82][mu][signal][round - 1] for signal in signals]
# aux83 = [statistics[sim][83][mu][signal][round - 1] for signal in signals]
# aux84 = [statistics[sim][84][mu][signal][round - 1] for signal in signals]
# aux85 = [statistics[sim][85][mu][signal][round - 1] for signal in signals]
# aux86 = [statistics[sim][86][mu][signal][round - 1] for signal in signals]
# aux87 = [statistics[sim][87][mu][signal][round - 1] for signal in signals]
# aux88 = [statistics[sim][88][mu][signal][round - 1] for signal in signals]
# aux89 = [statistics[sim][89][mu][signal][round - 1] for signal in signals]
# aux90 = [statistics[sim][90][mu][signal][round - 1] for signal in signals]
# aux91 = [statistics[sim][91][mu][signal][round - 1] for signal in signals]
# aux92 = [statistics[sim][92][mu][signal][round - 1] for signal in signals]
# aux93 = [statistics[sim][93][mu][signal][round - 1] for signal in signals]
# aux94 = [statistics[sim][94][mu][signal][round - 1] for signal in signals]
# aux95 = [statistics[sim][95][mu][signal][round - 1] for signal in signals]
# aux96 = [statistics[sim][96][mu][signal][round - 1] for signal in signals]
# aux97 = [statistics[sim][97][mu][signal][round - 1] for signal in signals]
# aux98 = [statistics[sim][98][mu][signal][round - 1] for signal in signals]
# aux99 = [statistics[sim][99][mu][signal][round - 1] for signal in signals]
# aux100 = [statistics[sim][100][mu][signal][round - 1] for signal in signals]
# Lista que contiene los sumatorios de cada tipo de senales producidas a nivel de la poblacion global en cada muestra y ronda
summation_pop = []
# Lista que contiene los sumatorios de cada tipo de senales producidas a nivel de subpoblacion en cada muestra y ronda
summation_subpop_1 = []
summation_subpop_2 = []
# Sumando las senales de cada tipo
for i in range(len(aux1)):
# A nivel de la poblacion
summation_pop.append(aux1[i] + aux2[i] + aux3[i] +
aux4[i] + aux5[i] + aux6[i] +
aux7[i] + aux8[i] + aux9[i] +
aux10[i])
# +
# aux11[i] + aux12[i] + aux13[i] + aux14[i] + aux15[i] + aux16[i] + aux17[i] + aux18[
# i] + aux19[i] + aux20[i] +
# aux21[i] + aux22[i] + aux23[i] + aux24[i] + aux25[i] + aux26[i] + aux27[i] + aux28[
# i] + aux29[i] + aux30[i] +
# aux31[i] + aux32[i] + aux33[i] + aux34[i] + aux35[i] + aux36[i] + aux37[i] + aux38[
# i] + aux39[i] + aux40[i] +
# aux41[i] + aux42[i] + aux43[i] + aux44[i] + aux45[i] + aux46[i] + aux47[i] + aux48[
# i] + aux49[i] + aux50[i] +
# aux51[i] + aux52[i] + aux53[i] + aux54[i] + aux55[i] + aux56[i] + aux57[i] + aux58[
# i] + aux59[i] + aux60[i] +
# aux61[i] + aux62[i] + aux63[i] + aux64[i] + aux65[i] + aux66[i] + aux67[i] + aux68[
# i] + aux69[i] + aux70[i] +
# aux71[i] + aux72[i] + aux73[i] + aux74[i] + aux75[i] + aux76[i] + aux77[i] + aux78[
# i] + aux79[i] + aux80[i] +
# aux81[i] + aux82[i] + aux83[i] + aux84[i] + aux85[i] + aux86[i] + aux87[i] + aux88[
# i] + aux89[i] + aux90[i] +
# aux91[i] + aux92[i] + aux93[i] + aux94[i] + aux95[i] + aux96[i] + aux97[i] + aux98[
# i] + aux99[i] + aux100[i])
# A nivel de las subpoblaciones
for i in range(len(aux1)):
summation_subpop_1.append(aux1[i] + aux2[i] + aux3[i] +
aux4[i] + aux5[i])
summation_subpop_2.append(+aux6[i] + aux7[i] +
aux8[i] + aux9[i] + aux10[i])
#print(aux1)
#output.append(shannon(summation_pop))
#print(output)
writer.writerow([
sim + 1, mu + 1, agent, menLen, round, condition,
i_power, samples[mu]['cont'], samples[mu]['coord'],
samples[mu]['mut']
] + aux + [summation_pop] + [shannon(summation_pop)] +
[shannon(summation_subpop_1)] +
[shannon(summation_subpop_2)] +
[summation_subpop_1] +
[summation_subpop_2] +
[brillouin_d(summation_pop)] +
[margalef(summation_pop)] +
[simpson(summation_pop)] +
[simpson_e(summation_pop)] +
[observed_otus(summation_pop) / 10])
def mobtyper_plasmid_summarize(mobtyper):
summary = {}
for sample_id in mobtyper:
plasmids = mobtyper[sample_id]
for plasmid_id in plasmids:
data = plasmids[plasmid_id]
if not plasmid_id in summary:
summary[plasmid_id] = {
'replicons': {},
'relaxases': {},
'overall_mobility': '',
'mobility': {
'conjugative': 0,
'mobilizable': 0,
'non-mobilizable': 0
},
'overall_serovar': '',
'serovar': {},
'continent': {},
'country': {},
'primary_sample_category': {},
'secondary_sample_category': {},
'associated_taxa': {},
'earliest_year': 0,
'year': {},
'samples': [],
'total_samples': 0,
'num_resistant': 0,
'proportion_resistant': 0,
'resistance_genes': {},
'serovar_entropy': -1,
'serovar_shannon_index': -1,
'serovar_simpson_index': -1,
'serovar_simpson_index_e': -1,
'serovar_chao1': 0,
'num_serovars': 0,
'poportion_human': 0,
'taxa_entropy': -1,
'taxa_shannon_index': -1,
'taxa_simpson_index': -1,
'taxa_simpson_index_e': -1,
'taxa_chao1': -1,
}
summary[plasmid_id]['total_samples'] += 1
summary[plasmid_id]['samples'].append(sample_id)
mobility = data['predicted_mobility']
summary[plasmid_id]['mobility'][mobility] += 1
rep = data['rep_type(s)'].split(",")
for r in rep:
if r not in summary[plasmid_id]['replicons']:
summary[plasmid_id]['replicons'][r] = 0
summary[plasmid_id]['replicons'][r] += 1
mob = data['relaxase_type(s)'].split(",")
for m in mob:
if m not in summary[plasmid_id]['relaxases']:
summary[plasmid_id]['relaxases'][m] = 0
summary[plasmid_id]['relaxases'][m] += 1
res_genes = data['resistance_genes']
if len(res_genes) > 0:
summary[plasmid_id]['num_resistant'] += 1
for gene_id in res_genes:
if not gene_id in summary[plasmid_id]['resistance_genes']:
summary[plasmid_id]['resistance_genes'][gene_id] = 0
summary[plasmid_id]['resistance_genes'][
gene_id] += res_genes[gene_id]
if not 'metadata' in data:
continue
for field_id in data['metadata']:
value = data['metadata'][field_id]
if value == 'nan' or value == '':
value = 'unknown'
if not field_id in summary[plasmid_id]:
continue
if field_id == 'associated_taxa':
for v in value:
if v == '' or v == 'nan':
continue
if not v in summary[plasmid_id][field_id]:
summary[plasmid_id][field_id][v] = 0
summary[plasmid_id][field_id][v] += 1
continue
if field_id in ('resistance_genes'):
continue
if not value in summary[plasmid_id][field_id]:
summary[plasmid_id][field_id][value] = 0
summary[plasmid_id][field_id][value] += 1
for plasmid_id in summary:
serovar_counts = list(summary[plasmid_id]['serovar'].values())
if len(summary[plasmid_id]['year']) > 0:
summary[plasmid_id]['earliest_year'] = min(
list(summary[plasmid_id]['year'].keys()))
if 'human' in summary[plasmid_id]['primary_sample_category']:
value = summary[plasmid_id]['primary_sample_category']['human']
else:
value = 0
summary[plasmid_id][
'poportion_human'] = value / summary[plasmid_id]['total_samples']
summary[plasmid_id]['num_serovars'] = len(
summary[plasmid_id]['serovar'])
summary[plasmid_id]['proportion_resistant'] = summary[plasmid_id][
'num_resistant'] / summary[plasmid_id]['total_samples']
summary[plasmid_id]['overall_mobility'] = max(
summary[plasmid_id]['mobility'],
key=summary[plasmid_id]['mobility'].get)
if len(summary[plasmid_id]['serovar']) > 0:
summary[plasmid_id]['overall_serovar'] = max(
summary[plasmid_id]['serovar'],
key=summary[plasmid_id]['serovar'].get)
if len(serovar_counts) > 0 and sum(serovar_counts) >= 10:
summary[plasmid_id]['serovar_entropy'] = calc_shanon_entropy(
serovar_counts)
summary[plasmid_id]['serovar_shannon_index'] = alpha.shannon(
serovar_counts)
summary[plasmid_id]['serovar_simpson_index'] = alpha.simpson(
serovar_counts)
summary[plasmid_id]['serovar_simpson_index_e'] = alpha.simpson_e(
serovar_counts)
summary[plasmid_id]['serovar_chao1'] = alpha.chao1(serovar_counts)
else:
print("{}\t{}".format(plasmid_id, sum(serovar_counts)))
print(summary[plasmid_id])
human_removed_taxa = {}
for taxon in summary[plasmid_id]['associated_taxa']:
if taxon == 'h**o sapiens':
continue
human_removed_taxa[taxon] = summary[plasmid_id]['associated_taxa'][
taxon]
taxa_counts = list(human_removed_taxa.values())
if len(taxa_counts) > 0 and sum(taxa_counts) >= 10:
summary[plasmid_id]['taxa_entropy'] = calc_shanon_entropy(
taxa_counts)
summary[plasmid_id]['taxa_shannon_index'] = alpha.shannon(
taxa_counts)
summary[plasmid_id]['taxa_simpson_index'] = alpha.simpson(
taxa_counts)
summary[plasmid_id]['taxa_simpson_index_e'] = alpha.simpson_e(
taxa_counts)
summary[plasmid_id]['taxa_chao1'] = alpha.chao1(taxa_counts)
return summary
def main(menLen, i_power, sigmas, samples, simulations, condition):
agents = [1,2,3,4,5,6,7,8,9,10]
signals = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10']
network = group(agents)
pairs = [list(elem) for elem in network]
statistics = {
sim: {
agent: {
sample: {
signal: [0 for round in range(1, len(pairs) + 1)]
for signal in signals
}
for sample in range(len(samples))
}
for agent in agents
}
for sim in range(simulations)
}
for sim in range(simulations):
#network = group(agents)
#pairs = [list(elem) for elem in network]
for mu in range(len(samples)):
game = Match(
agents,
pairs,
signals,
sigmas,
samples[mu]["cont"],
samples[mu]["coord"],
samples[mu]["mut"],
menLen,
i_power
)
game.play()
for n, round in enumerate(game.memory):
for agent, signal in round.items():
statistics[sim][agent][mu][signal][n] += 1
with open('homogeneity_C_0.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=',',
quotechar='"', quoting=csv.QUOTE_MINIMAL)
writer.writerow(['Simulation', 'Sample', 'Agent', 'Memory', 'Generation', 'Condition', 'Inst_power','Content bias',
'Coordination bias','Mutation rate'] + signals +
['Population signals'] + ['Entropy_population'] + ['Entropy_subpopulation_1'] + [
'Entropy_subpopulation_2'] + ['Subpopulation_1 signals'] + ['Subpopulation_2 signals']
+ ['Brillouin_population'] + ['Margalef_population'] + ['Simpson_population'] + [
'Simpson_e_population'] + ['Richness'])
# Creando listas que contienen la produccion de cada senal: para toda la poblacion (aux) y para cada jugador (auxn)
#for agent in agents:
for sim in range(simulations):
for mu in range(len(samples)):
for round in range(1, len(pairs) + 1):
aux = [statistics[sim][agent][mu][signal][round - 1] for signal in signals]
aux1 = [statistics[sim][1][mu][signal][round - 1] for signal in signals]
aux2 = [statistics[sim][2][mu][signal][round - 1] for signal in signals]
aux3 = [statistics[sim][3][mu][signal][round - 1] for signal in signals]
aux4 = [statistics[sim][4][mu][signal][round - 1] for signal in signals]
aux5 = [statistics[sim][5][mu][signal][round - 1] for signal in signals]
aux6 = [statistics[sim][6][mu][signal][round - 1] for signal in signals]
aux7 = [statistics[sim][7][mu][signal][round - 1] for signal in signals]
aux8 = [statistics[sim][8][mu][signal][round - 1] for signal in signals]
aux9 = [statistics[sim][9][mu][signal][round - 1] for signal in signals]
aux10 = [statistics[sim][10][mu][signal][round - 1] for signal in signals]
# Lista que contiene los sumatorios de cada tipo de senales producidas a nivel de la poblacion global en cada muestra y ronda
summation_pop = []
# Lista que contiene los sumatorios de cada tipo de senales producidas a nivel de subpoblacion en cada muestra y ronda
summation_subpop_1 = []
summation_subpop_2 = []
# Sumando las senales de cada tipo
for i in range(len(aux1)):
# A nivel de la poblacion
summation_pop.append(
aux1[i] + aux2[i] + aux3[i] + aux4[i] + aux5[i] + aux6[i] + aux7[i] + aux8[i] + aux9[i] + aux10[i])
# A nivel de las subpoblaciones
for i in range(len(aux1)):
summation_subpop_1.append(aux1[i] + aux2[i] + aux3[i] + aux4[i] + aux5[i])
summation_subpop_2.append(+ aux6[i] + aux7[i] + aux8[i] + aux9[i] + aux10[i])
#print(aux1)
#output.append(shannon(summation_pop))
#print(output)
writer.writerow([sim + 1, mu + 1, agent, menLen, round, condition, i_power, samples[mu]['cont'],
samples[mu]['coord'],
samples[mu]['mut']] + aux + [summation_pop] + [shannon(summation_pop)] + [
shannon(summation_subpop_1)] + [shannon(summation_subpop_2)] + [
summation_subpop_1] + [summation_subpop_2]
+ [brillouin_d(summation_pop)] + [margalef(summation_pop)] + [
simpson(summation_pop)] + [simpson_e(summation_pop)] + [
observed_otus(summation_pop) / 10])
def main():
# Agents
agents = [1, 2, 3, 4, 5, 6, 7, 8]
# Variants
signals = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8']
# Pair composition
# Randomize pair composition:
network = group(agents)
pairs = [list(elem) for elem in network]
# Memory length
menLen = 3
# Condition
condition = "Heterogeneity C W"
#### STRUCTURE OF VALUE SYSTEMS IN THE MICRO-SOCIETY (each value system (s1, s2, etc...) is assigned an agent):
#### class Match, in the initialization, creates "agents" and passes the parameters to each agent,
#### (including their sigmas). For this reason, a dictionary containig keys (agents' names ("name") and
#### values (sigmas), is required
s1 = [1, 1, 0, 0, 0, 0, 0, 0]
s2 = [0, 0, 0, 0, 0, 0, 1, 1]
sigmas = {1: s1, 2: s1, 3: s1, 4: s1, 5: s2, 6: s2, 7: s2, 8: s2}
# Content bias ('cont'): no content bias=0.0, fully content biased population=1.0
# Coordination bias ('coord'): fully egocentric=0.0, fully allocentric=1.0, neutral=0.5
# mutation rate ('mut')
# Setup for one parameter combination:
#samples = [{'cont': 0.2, 'coord': 0.5, 'mut': 0.02} for _ in range(100)]
# Setup for all parameter combinations (content bias and coordination bias):
samples = [
# {'cont': 0.0, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.0, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.0, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.0, 'mut': 0.02}]
# {'cont': 0.0, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.1, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.1, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.2, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.2, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.3, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.3, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.4, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.4, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.4, 'mut': 0.02},
{
'cont': 0.0,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.1,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.2,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.3,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.4,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.5,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.6,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.7,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.8,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 0.9,
'coord': 0.5,
'mut': 0.02
},
{
'cont': 1.0,
'coord': 0.5,
'mut': 0.02
}
]
# {'cont': 0.0, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.6, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.6, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.7, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.7, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.8, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.8, 'mut': 0.02},
# {'cont': 0.0, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.1, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.2, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.3, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.4, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.5, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.6, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.7, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.8, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.9, 'coord': 0.9, 'mut': 0.02},
# {'cont': 1.0, 'coord': 0.9, 'mut': 0.02},
# {'cont': 0.0, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.1, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.2, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.3, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.4, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.5, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.6, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.7, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.8, 'coord': 1.0, 'mut': 0.02},
# {'cont': 0.9, 'coord': 1.0, 'mut': 0.02},
# {'cont': 1.0, 'coord': 1.0, 'mut': 0.02}]
samples = [d for d in samples for _ in range(1)]
# Number of runs (simulations)
simulations = 250
# Generating statistics
statistics = {
sim: {
agent: {
sample: {
signal: [0 for round in range(1,
len(pairs) + 1)]
for signal in signals
}
for sample in range(len(samples))
}
for agent in agents
}
for sim in range(simulations)
}
# Running The Game (game.play)
for sim in range(simulations):
network = group(agents)
pairs = [list(elem) for elem in network]
for mu in range(len(samples)):
game = Match(agents, pairs, signals, sigmas, samples[mu]["cont"],
samples[mu]["coord"], samples[mu]["mut"], menLen)
game.play()
for n, round in enumerate(game.memory):
for agent, signal in round.items():
statistics[sim][agent][mu][signal][n] += 1
# Writing data
with open('heterogeneity_C_W.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile,
delimiter=';',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow([
'Simulation', 'Sample', 'Agent', 'Memory', 'Generation',
'Condition', 'Content bias', 'Coordination bias', 'Mutation rate'
] + signals + ['Population signals'] + ['Entropy_population'] +
['Entropy_subpopulation_1'] +
['Entropy_subpopulation_2'] +
['Subpopulation_1 signals'] +
['Subpopulation_2 signals'] +
['Brillouin_population'] + ['Margalef_population'] +
['Simpson_population'] + ['Simpson_e_population'] +
['Richness'])
# Creating lists that contain signals' production
for agent in agents:
for sim in range(simulations):
for mu in range(len(samples)):
for round in range(1, len(pairs) + 1):
aux = [
statistics[sim][agent][mu][signal][round - 1]
for signal in signals
]
aux1 = [
statistics[sim][1][mu][signal][round - 1]
for signal in signals
]
aux2 = [
statistics[sim][2][mu][signal][round - 1]
for signal in signals
]
aux3 = [
statistics[sim][3][mu][signal][round - 1]
for signal in signals
]
aux4 = [
statistics[sim][4][mu][signal][round - 1]
for signal in signals
]
aux5 = [
statistics[sim][5][mu][signal][round - 1]
for signal in signals
]
aux6 = [
statistics[sim][6][mu][signal][round - 1]
for signal in signals
]
aux7 = [
statistics[sim][7][mu][signal][round - 1]
for signal in signals
]
aux8 = [
statistics[sim][8][mu][signal][round - 1]
for signal in signals
]
# List that contains the sum of produced variants in the population for each simulation and round
summation_pop = []
# List that contains the sum of produced vatiants in each population for each simulation and round
summation_subpop_1 = []
summation_subpop_2 = []
# Summing variants
for i in range(len(aux1)):
# In teh population
summation_pop.append(aux1[i] + aux2[i] + aux3[i] +
aux4[i] + aux5[i] + aux6[i] +
aux7[i] + aux8[i])
# In each subpopulation
for i in range(len(aux1)):
summation_subpop_1.append(aux1[i] + aux2[i] +
aux3[i] + aux4[i])
summation_subpop_2.append(aux5[i] + aux6[i] +
aux7[i] + aux8[i])
writer.writerow([
sim + 1, mu +
1, agent, menLen, round, condition, samples[mu]
['cont'], samples[mu]['coord'], samples[mu]['mut']
] + aux + [summation_pop] + [entropy(summation_pop)] +
[entropy(summation_subpop_1)] +
[entropy(summation_subpop_2)] +
[summation_subpop_1] +
[summation_subpop_2] +
[brillouin_d(summation_pop)] +
[margalef(summation_pop)] +
[simpson(summation_pop)] +
[simpson_e(summation_pop)] +
[observed_otus(summation_pop) / 8])
def main():
# Agents names (and number of agents):
agents = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Variants
signals = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10']
# Social network (order in which agents pair over time)
network = group(agents)
pairs = [list(elem) for elem in network]
# Memory length (amount of hisotry (in rounds) that agents are able to recall)
menLen = 3
# Condition (type name according to value system structure, sigmas)
condition = "Homogeneity"
# Scenario (type name according to value system structure sigmas)
scenario = "OTA"
# Institutional power (type number according to value assinged to institutional power in "choose" method
i_power = 0
####SIGMAS: Agents' value system at the initial state. That is, the value that an agent assigns to each signal in the initial state####
### Homogeneity and hegemony (OTA)
s1 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
s2 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
### Heterogeneity and pseudo-random (PR)
#s1 = np.random.uniform(low=0, high=1, size=(10,))
#s2 = np.random.uniform(low=0, high=1, size=(10,))
### Dictionary of agents' value systems
sigmas = {1: s1, 2: s1, 3: s1, 4: s1, 5: s1, 6: s2, 7: s2, 8: s2, 9:s2, 10:s2}
# Samples: agents' content bias, coordination bias, conformity bias, confirmation bias, innovation rate.
# Content bias ('cont'): no content bias=0.0, fully content biased population=1.0
# Coordination bias ('coord'): fully egocentric=0.0, fully allocentric=1.0, neutral=0.5
# Compliance bias ('conform'): null compliance=0.0, fully compliant=1.0
# Conformity bias ('conformity'): null conformity=0.0, full conformity=1.0
# Innovation rate('mut')
# Setup for different parameter combinations:
samples = [
{'cont': 0.0, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 0, 'confirm':0, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 0, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 0, 'confirm': 1, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 0.5, 'confirm': 0, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 0.5, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 0.5, 'confirm': 1, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 1, 'confirm': 0, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 1, 'confirm': 0.5, 'mut': 0.02},
{'cont': 0.0, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02},
{'cont': 0.2, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02},
{'cont': 0.4, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02},
{'cont': 0.5, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02},
{'cont': 0.6, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02},
{'cont': 0.8, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02},
{'cont': 1.0, 'coord': 0.5, 'conform': 1, 'confirm': 1, 'mut': 0.02}]
# Number of samples of each parameter combination
samples = [d for d in samples for _ in range(1)]
# Number of simulations
simulations = 1
# Statistics
statistics = {
sim: {
agent: {
sample: {
signal: [0 for round in range(1, len(pairs) + 1)]
for signal in signals
}
for sample in range(len(samples))
}
for agent in agents
}
for sim in range(simulations)
}
# Piece of code to run each instance of the game (game.play) for the specified number of samples and simulations
for sim in range(simulations):
network = group(agents)
pairs = [list(elem) for elem in network]
for mu in range(len(samples)):
game = Match(
agents,
pairs,
signals,
sigmas,
samples[mu]["cont"],
samples[mu]["coord"],
samples[mu]["conform"],
samples[mu]["confirm"],
samples[mu]["mut"],
menLen
)
game.play()
for n, round in enumerate(game.memory):
for agent, signal in round.items():
statistics[sim][agent][mu][signal][n] += 1
# Write csv file
with open('Test_COEVO_Hom_OTA_R_I00_F.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=',',
quotechar='"', quoting=csv.QUOTE_MINIMAL)
writer.writerow(['Simulation', 'Sample', 'Agent', 'Memory', 'Generation', 'Condition', 'Scenario', 'Inst_power','Content bias',
'Coordination bias','Conformity bias','Confirmation bias', 'Mutation rate'] + signals +
['Population signals'] + ['Entropy_population'] + ['Entropy_subpopulation_1'] + [
'Entropy_subpopulation_2'] + ['Subpopulation_1 signals'] + ['Subpopulation_2 signals']
+ ['Brillouin_population'] + ['Margalef_population'] + ['Simpson_population'] + [
'Simpson_e_population'] + ['Richness'])
# Creating lists that contain the the production of signals at each round: for the whole population (aux) and each agent (auxn)
for agent in agents:
for sim in range(simulations):
for mu in range(len(samples)):
for round in range(1, len(pairs) + 1):
aux = [statistics[sim][agent][mu][signal][round - 1] for signal in signals]
aux1 = [statistics[sim][1][mu][signal][round - 1] for signal in signals]
aux2 = [statistics[sim][2][mu][signal][round - 1] for signal in signals]
aux3 = [statistics[sim][3][mu][signal][round - 1] for signal in signals]
aux4 = [statistics[sim][4][mu][signal][round - 1] for signal in signals]
aux5 = [statistics[sim][5][mu][signal][round - 1] for signal in signals]
aux6 = [statistics[sim][6][mu][signal][round - 1] for signal in signals]
aux7 = [statistics[sim][7][mu][signal][round - 1] for signal in signals]
aux8 = [statistics[sim][8][mu][signal][round - 1] for signal in signals]
aux9 = [statistics[sim][9][mu][signal][round - 1] for signal in signals]
aux10 = [statistics[sim][10][mu][signal][round - 1] for signal in signals]
# List that contains the summation of produced signals at the level of the population
summation_pop = []
# # List that contains the summation of produced signals at the level of each subpopulation
summation_subpop_1 = []
summation_subpop_2 = []
# Piece of code to append the lists of signals
for i in range(len(aux1)):
# At the population level
summation_pop.append(
aux1[i] + aux2[i] + aux3[i] + aux4[i] + aux5[i] + aux6[i] + aux7[i] + aux8[i] + aux9[i] + aux10[i])
# At the subpopulation level
for i in range(len(aux1)):
summation_subpop_1.append(aux1[i] + aux2[i] + aux3[i] + aux4[i])
summation_subpop_2.append(aux5[i] + aux6[i] + aux7[i] + aux8[i])
# Writing csv file
writer.writerow([sim + 1, mu + 1, agent, menLen, round, condition, scenario, i_power, samples[mu]['cont'],
samples[mu]['coord'], samples[mu]['conform'], samples[mu]['confirm'],
samples[mu]['mut']] + aux + [summation_pop] + [shannon(summation_pop)] + [
shannon(summation_subpop_1)] + [shannon(summation_subpop_2)] + [
summation_subpop_1] + [summation_subpop_2]
+ [brillouin_d(summation_pop)] + [margalef(summation_pop)] + [
simpson(summation_pop)] + [simpson_e(summation_pop)] + [
observed_otus(summation_pop) / 8])