import pandas as pd 

from ChildProject.projects import ChildProject

from os.path import join as opj, basename

corpora = [
    "input/bergelson",
    "input/warlaumont",
    "input/winnipeg",
    "input/lucid"
]

dic = {
    "input/bergelson": "confidential/original/bergelson_dict.csv",
    "input/lucid": "confidential/original/lucid_dict.csv",
    "input/warlaumont": "original/warlaumont_dict_matched.csv",
    "input/winnipeg": "confidential/original/winnipeg_dict_matched.csv"
}

correspondance = {
    "BER": "input/bergelson",
    "ROW": "input/lucid",
    "SOD": "input/winnipeg",
    "WAR": "input/warlaumont"
}

projects = [ 
    ChildProject(corpus) for corpus in corpora
]

for project in projects:
    project.read()

recordings = pd.concat([
    projects[i].recordings.assign(corpus=corpus)
    for i, corpus in enumerate(corpora)
])

recordings["its_filename"] = recordings["its_filename"].str.replace(".its", "")

aclew_id = pd.concat([
    pd.read_csv(opj(corpus, "metadata", dic[corpus])).assign(corpus=corpus)
    for corpus in corpora
])

aclew_id["its"] = aclew_id["its"].str.replace(".its", "")

aclew_md = pd.read_csv("input/aclew_md.csv")

recordings = recordings[["corpus", "child_id", "recording_filename", "its_filename"]].merge(
    aclew_id[["corpus", "its", "aclew_id"]],
    how="inner",
    left_on=["corpus", "its_filename"],
    right_on=["corpus", "its"]
)

recordings = recordings.merge(aclew_md, how="inner", left_on="aclew_id", right_on="aclew_id")
children = recordings.groupby(["corpus", "child_id"]).agg(n_siblings=("number_older_sibs", "max"))
children = children.reset_index()
children["corpus"] = children.corpus.map(basename)
children.to_csv("input/siblings.csv")