import csv
import itertools
import sys

PROBS = {

    "gene": {
        2: 0.01,
        1: 0.03,
        0: 0.96
    },

    "trait": {

        2: {
            True: 0.65,
            False: 0.35
        },

        1: {
            True: 0.56,
            False: 0.44
        },

        0: {
            True: 0.01,
            False: 0.99
        }
    },

    "mutation": 0.01
}


def main():
    if len(sys.argv) != 2:
        sys.exit("Usage: python heredityarya.py data.csv")
    people = load_data(sys.argv[1])

    probabilities = {
        person: {
            "gene": {
                2: 0,
                1: 0,
                0: 0
            },
            "trait": {
                True: 0,
                False: 0
            }
        }
        for person in people
    }

    names = set(people)
    for have_trait in powerset(names):

        fails_evidence = any(
            (people[person]["trait"] is not None and
             people[person]["trait"] != (person in have_trait))
            for person in names
        )
        if fails_evidence:
            continue

        for one_gene in powerset(names):
            for two_genes in powerset(names - one_gene):

                p = joint_probability(people, one_gene, two_genes, have_trait)
                update(probabilities, one_gene, two_genes, have_trait, p)

    normalize(probabilities)

    for person in people:
        [b]print(f" {person}:")[/b]
        for field in probabilities[person]:
            print(f"  {field.capitalize()}:")
            for value in probabilities[person][field]:
                p = probabilities[person][field][value]
                print(f"    {value}: {p:.4f}")

def load_data(filename):
    data = dict()
    with open(filename) as f:
        reader = csv.DictReader(f)
        for row in reader:
            name = row["name"]
            data[name] = {
                "name": name,
                "mother": row["mother"] or None,
                "father": row["father"] or None,
                "trait": (True if row["trait"] == "1" else
                          False if row["trait"] == "0" else None)
            }
    return data


def powerset(s):
    s = list(s)
    return [
        set(s) for s in itertools.chain.from_iterable(
            itertools.combinations(s, r) for r in range(len(s) + 1)
        )
    ]


def joint_probability(people, one_gene, two_genes, have_trait):
    probability = 1
    for person in people:

        num_genes = num_genes_of_person(person, one_gene, two_genes)

        has_trait = person in have_trait

        if people[person]['mother'] is None and people[person]['father'] is None:
            probability *= PROBS["gene"][num_genes] * PROBS["trait"][num_genes][has_tr

            else:
            num_genes_mother = num_genes_of_person(people[person]['mother'], one_gene
            num_genes_father = num_genes_of_person(people[person]['father'], one_gene

            if num_genes == 0:
                probability *= probability_inheritence(num_genes_mother, False) * prob

            elif num_genes == 1:
                probability *= probability_inheritence(num_genes_mother, True) * proba
                               + probability_inheritence(num_genes_mother, False) *

            elif num_genes == 2:
                probability *= probability_inheritence(num_genes_mother, True) * proba

            probability *= PROBS["trait"][num_genes][has_trait]
    return probability

def update(probabilities, one_gene, two_genes, have_trait, p):
    for person in probabilities:

        num_genes = num_genes_of_person(person, one_gene, two_genes)

        has_trait = person in have_trait

        probabilities[person]["gene"][num_genes] += p
        probabilities[person]["trait"][has_trait] += p

def normalize(probabilities):
    for person in probabilities:
        trait_sum = sum(probabilities[person]["trait"].values())
        gene_sum = sum(probabilities[person]["gene"].values())

        for gene in probabilities[person]["gene"]:
            probabilities[person]["gene"][gene] /= gene_sum

        for trait in probabilities[person]["trait"]:
            probabilities[person]["trait"][trait] /= trait_sum

if __name__ == "__main__":
    main()

people = ['me', 'you', 'him', 'her', 'they']
for person in people:
    print(f" {person}:")

(Nov-03-2020, 06:36 PM)Axel_Erfurt Wrote: you need to use python >= 3.6 for f-strings

(Nov-03-2020, 07:11 PM)deanhystad Wrote: Does this work?

people = ['me', 'you', 'him', 'her', 'they']
for person in people:
    print(f" {person}:")

A syntax error may have nothing to do with the line that gets flagged. It could be an error that started some line above but only became noticeable at the reported line.

(Nov-03-2020, 07:04 PM)AryaIC Wrote: I'm just very nervous if I need to install python 3.6 as you suggested ...

(Nov-04-2020, 11:29 AM)Axel_Erfurt Wrote:

(Nov-03-2020, 07:04 PM)AryaIC Wrote: I'm just very nervous if I need to install python 3.6 as you suggested ...

python >= 3.6

means 3.6 or higher

if len(sys.argv) != 2:
    sys.exit("Usage: python heredityarya.py data.csv")

(Nov-05-2020, 09:15 PM)Axel_Erfurt Wrote: you have

if len(sys.argv) != 2:
    sys.exit("Usage: python heredityarya.py data.csv")

to use f-strings you should start it with

python3 heredityarya.py data.csv