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import json
import os
import sys
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from functools import partial
from re import sub
# Required Evils!
from flask import g
from wqflask import app
from wqflask.api.gen_menu import gen_dropdown_json
from wqflask.show_trait import show_trait
from wqflask.database import database_connection
from wqflask.search_results import SearchResultPage
class UserSessionSimulator():
def __init__(self, user_id):
self._user_id = user_id
@property
def user_id(self):
return self._user_id
def camel_case(string):
s = sub(r"(_|-)+", " ", string).title().replace(" ", "")
return ''.join([s[0].lower(), s[1:]])
def dump_sample_data(dataset_name, trait_id):
"""Given a DATASET_NAME e.g. 'BXDPublish' and a TRAIT_ID
e.g. '10007', dump the sample data as json object"""
with database_connection() as conn, conn.cursor() as cursor:
sample_data = {"headers": ["Name", "Value", "SE"], "data": []}
with app.app_context():
g.user_session = UserSessionSimulator(None)
data = show_trait.ShowTrait(
cursor, user_id=None,
kw={
"trait_id": trait_id,
"dataset": dataset_name
}
)
attributes = data.js_data.get("attributes")
for id_ in attributes:
sample_data["headers"].append(attributes[id_].name)
for sample in data.js_data.get("sample_lists")[0]:
sample_data["data"].append(
[
sample.name,
sample.value or 'x',
sample.variance or 'x',
*[str(sample.extra_attributes.get(str(key), "x"))
for key in attributes],
])
return sample_data
def fetch_all_traits(species, group, type_, dataset):
with app.app_context():
g.user_session = UserSessionSimulator(None)
for result in SearchResultPage({
"species": species,
"group": group,
"type": type_,
"dataset": dataset,
"search_terms_or": "*",
}).trait_list:
yield result.get('name') or result.get('display_name')
def get_trait_metadata(dataset_name, trait_id):
with database_connection() as conn, conn.cursor() as cursor:
with app.app_context():
g.user_session = UserSessionSimulator(None)
data = show_trait.ShowTrait(
cursor, user_id=None,
kw={
"trait_id": trait_id,
"dataset": dataset_name,
}).this_trait.__dict__
data.pop("data")
data.pop("comments")
# filter any emply values and camelCase the keys
_d = {camel_case(key): value for key, value in data.items() if value}
_d["dataset"] = dataset_name
return _d
def dump_json(base_dir, dataset_name, trait):
print(f"\033[FDumping: {dataset_name}/{trait}]")
with open(os.path.join(base_dir, f"{trait}.json"), "w") as f:
_data = dump_sample_data(dataset_name, trait)
_data["metadata"] = get_trait_metadata(dataset_name, trait)
json.dump(_data, f)
def dump_dataset(target_dir, species, group_name, dataset_type, dataset):
start = time.perf_counter()
dataset_name = dataset[1]
if not os.path.isdir(
BASE_DIR := os.path.join(
target_dir,
dataset_name
)
):
os.makedirs(BASE_DIR)
_l = len(f"Dumping {dataset_name} into {target_dir}:")
print(f"""
{'='*_l}
Dumping {dataset_name} into {sys.argv[1]}:
{'='*_l}
""")
with ThreadPoolExecutor() as executor:
executor.map(
partial(
dump_json,
BASE_DIR,
dataset_name
),
fetch_all_traits(
species=species,
group=group_name,
type_=dataset_type,
dataset=dataset_name
)
)
print(f"\033[FDONE DUMPING: {BASE_DIR} !!")
finish = time.perf_counter()
print(f"It took {finish-start: .2f} second(s) to finish")
def main():
# Dump all sampledata into a given directory
with database_connection() as conn:
for species, group in gen_dropdown_json(conn).get("datasets").items():
for group_name, type_ in group.items():
for dataset_type, datasets in type_.items():
with ProcessPoolExecutor() as p_exec:
p_exec.map(
partial(
dump_dataset,
sys.argv[1],
species,
group_name,
dataset_type
),
datasets
)
if __name__ == "__main__":
main()
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