import collections
import glob
import os
from os import path

import matplotlib_venn
import pandas as pd

rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')

OLD_VERSION = '332'
NEW_VERSION = '333'

old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(NEW_VERSION)))

"""
Explanation: Author: Pascal, pascal@bayesimpact.org
Date: 2017-11-08
ROME update from v332 to v333
In November 2017 a new version of the ROME was realeased. I want to investigate what changed and whether we need to do anything about it.
You might not be able to reproduce this notebook, mostly because it requires to have the two versions of the ROME in your data/rome/csv folder which happens only just before we switch to v333. You will have to trust me on the results ;-)
Skip the run test because it requires older versions of the ROME.
End of explanation
"""


new_files = new_version_files - frozenset(f.replace(OLD_VERSION, NEW_VERSION) for f in old_version_files)
deleted_files = old_version_files - frozenset(f.replace(NEW_VERSION, OLD_VERSION) for f in new_version_files)

print('{:d} new files'.format(len(new_files)))
print('{:d} deleted files'.format(len(deleted_files)))

"""
Explanation: First let's check if there are new or deleted files (only matching by file names).
End of explanation
"""


# Load all ROME datasets for the two versions we compare.
VersionedDataset = collections.namedtuple('VersionedDataset', ['basename', 'old', 'new'])
rome_data = [VersionedDataset(
        basename=path.basename(f),
        old=pd.read_csv(f.replace(NEW_VERSION, OLD_VERSION)),
        new=pd.read_csv(f))
    for f in sorted(new_version_files)]

def find_rome_dataset_by_name(data, partial_name):
    for dataset in data:
        if 'unix_{}_v{}_utf8.csv'.format(partial_name, NEW_VERSION) == dataset.basename:
            return dataset
    raise ValueError('No dataset named {}, the list is\n{}'.format(partial_name, [d.basename for d in data]))

"""
Explanation: So we have the same set of files in both versions: good start.
Now let's set up a dataset that, for each table, links both the old and the new file together.
End of explanation
"""


for dataset in rome_data:
    if set(dataset.old.columns) != set(dataset.new.columns):
        print('Columns of {} have changed.'.format(dataset.basename))

"""
Explanation: Let's make sure the structure hasn't changed:
End of explanation
"""


same_row_count_files = 0
for dataset in rome_data:
    diff = len(dataset.new.index) - len(dataset.old.index)
    if diff > 0:
        print('{:d}/{:d} values added in {}'.format(diff, len(dataset.new.index), dataset.basename))
    elif diff < 0:
        print('{:d}/{:d} values removed in {}'.format(-diff, len(dataset.old.index), dataset.basename))
    else:
        same_row_count_files += 1
print('{:d}/{:d} files with the same number of rows'.format(same_row_count_files, len(rome_data)))

"""
Explanation: All files have the same columns as before: still good.
Now let's see for each file if there are more or less rows.
End of explanation
"""


jobs = find_rome_dataset_by_name(rome_data, 'referentiel_appellation')

new_jobs = set(jobs.new.code_ogr) - set(jobs.old.code_ogr)
obsolete_jobs = set(jobs.old.code_ogr) - set(jobs.new.code_ogr)
stable_jobs = set(jobs.new.code_ogr) & set(jobs.old.code_ogr)

matplotlib_venn.venn2((len(obsolete_jobs), len(new_jobs), len(stable_jobs)), (OLD_VERSION, NEW_VERSION));

"""
Explanation: There are some minor changes in many files, but based on my knowledge of ROME, none from the main files.
The most interesting ones are in referentiel_appellation, item, and liens_rome_referentiels, so let's see more precisely.
End of explanation
"""


pd.options.display.max_colwidth = 2000
jobs.new[jobs.new.code_ogr.isin(new_jobs)][['code_ogr', 'libelle_appellation_long', 'code_rome']]

"""
Explanation: Alright, so the only change seems to be 15 new jobs added. Let's take a look (only showing interesting fields):
End of explanation
"""


items = find_rome_dataset_by_name(rome_data, 'item')

new_items = set(items.new.code_ogr) - set(items.old.code_ogr)
obsolete_items = set(items.old.code_ogr) - set(items.new.code_ogr)
stable_items = set(items.new.code_ogr) & set(items.old.code_ogr)

matplotlib_venn.venn2((len(obsolete_items), len(new_items), len(stable_items)), (OLD_VERSION, NEW_VERSION));

"""
Explanation: They mostly seem related to the digital industry, e.g. we finally have a job for John, our UX Designer. But there are also few others.
OK, let's check at the changes in items:
End of explanation
"""


items.old[items.old.code_ogr.isin(obsolete_items)].tail()

items.new[items.new.code_ogr.isin(new_items)].head()

"""
Explanation: As anticipated it is a very minor change (hard to see it visually): some items are now obsolete and new ones have been created. Let's have a look.
End of explanation
"""


links = find_rome_dataset_by_name(rome_data, 'liens_rome_referentiels')
old_links_on_stable_items = links.old[links.old.code_ogr.isin(stable_items)]
new_links_on_stable_items = links.new[links.new.code_ogr.isin(stable_items)]

old = old_links_on_stable_items[['code_rome', 'code_ogr']]
new = new_links_on_stable_items[['code_rome', 'code_ogr']]

links_merged = old.merge(new, how='outer', indicator=True)
links_merged['_diff'] = links_merged._merge.map({'left_only': 'removed', 'right_only': 'added'})
links_merged._diff.value_counts()

"""
Explanation: Those entries look legitimate.
The changes in liens_rome_referentiels include changes for those items, so let's only check the changes not related to those.
End of explanation
"""


job_group_names = find_rome_dataset_by_name(rome_data, 'referentiel_code_rome').old.set_index('code_rome').libelle_rome
item_names = items.new.set_index('code_ogr').libelle.drop_duplicates()
links_merged['job_group_name'] = links_merged.code_rome.map(job_group_names)
links_merged['item_name'] = links_merged.code_ogr.map(item_names)
links_merged.dropna().head(10)

"""
Explanation: So in addition to the added and remove items, there are 48 fixes. Let's have a look:
End of explanation
"""


