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Feature/feather for principles #259

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Feature/feather for principles #259

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64d9c01
Externalize imports
orviz Mar 4, 2025
147f063
Define common _store() function
orviz Mar 4, 2025
0c4b5d0
Add method to store totals per-principle
orviz Mar 4, 2025
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43 changes: 31 additions & 12 deletions scripts/fair-eva.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,10 +23,12 @@
import argparse
import json
import logging
import os.path
import socket
import sys
import time

import pandas as pd
import requests
from flask_babel import Babel, gettext
from flask_babel import lazy_gettext as _l
Expand Down Expand Up @@ -523,17 +525,11 @@ def search(keytext):
sys.exit()


def store(identifier, score_data, file_format="feather", path="/tmp"):
import os.path

import pandas as pd

dframe = pd.DataFrame(score_data)
dframe.columns = ["fair_indicator", "fair_principle", "score", "message"]
dframe["score"] = pd.to_numeric(dframe["score"])
logging.debug("Resultant Pandas data frame: %s" % dframe)

file_name = "fair_eva_results-%s.%s" % (identifier, file_format)
def _store(identifier, dframe, file_format="feather", path="/tmp", file_prefix=""):
if file_prefix:
file_name = "fair_eva_results-%s-%s.%s" % (file_prefix, identifier, file_format)
else:
file_name = "fair_eva_results-%s.%s" % (identifier, file_format)
file_path = os.path.join(path, file_name)
if file_format not in ["feather", "csv"]:
logging.error("Output file format not supported: %s" % file_format)
Expand All @@ -544,8 +540,31 @@ def store(identifier, score_data, file_format="feather", path="/tmp"):
dframe.to_feather(file_path)
elif file_format in ["csv"]:
dframe.to_csv(file_path)
return file_path

logging.info("Stored FAIR assessment results to: %s" % file_path)

def store(identifier, score_data, file_format="feather", path="/tmp"):
dframe = pd.DataFrame(score_data)
dframe.columns = ["fair_indicator", "fair_principle", "score", "message"]
dframe["score"] = pd.to_numeric(dframe["score"])
logging.debug("Resultant Pandas data frame: %s" % dframe)
file_path = _store(
identifier, dframe, file_format, path, file_prefix="per-indicator"
)
logging.info("FAIR assessment results per-indicator stored in: %s" % file_path)


def store_totals(plugin, identifier, score_data, file_format="feather", path="/tmp"):
score_data_new = list(map(list, score_data.items()))
score_data_new = list(map(lambda item: [plugin, identifier] + item, score_data_new))
dframe = pd.DataFrame(score_data_new)
dframe.columns = ["identifier", "plugin", "fair_principle", "score"]
dframe["score"] = pd.to_numeric(dframe["score"])
logging.debug("Resultant Pandas data frame: %s" % dframe)
file_path = _store(
identifier, dframe, file_format, path, file_prefix="per-principle"
)
logging.info("FAIR assessment results per-principle stored in: %s" % file_path)


def main():
Expand Down