wenh06/cinc2022 repository overview

pd.read_excel("./results/final_scores.xlsx", engine="openpyxl", sheet_name=None)

from utils._final_results import get_team_digest

get_team_digest("Revenger", fmt="pd", latest=True)  # pandas DataFrame format
get_team_digest("Revenger", fmt="tex", latest=True)  # latex format (string)

# beautifulsoup4 and html5lib required
import pandas as pd

outcome_url = "https://docs.google.com/spreadsheets/u/0/d/e/2PACX-1vRNBATogMRsfio3938bU4r6fcAad85jNzTbSRtRhQ74xHw9shuYoP4uxkK6uKV1zw8CKjPC3AMm33qn/pubhtml/sheet?headers=false&gid=1883863848"
murmur_url = "https://docs.google.com/spreadsheets/u/0/d/e/2PACX-1vRNBATogMRsfio3938bU4r6fcAad85jNzTbSRtRhQ74xHw9shuYoP4uxkK6uKV1zw8CKjPC3AMm33qn/pubhtml/sheet?headers=false&gid=0"

df_outcome = pd.read_html(outcome_url, flavor="bs4", header=[1], index_col=[0])[0].reset_index(drop=True).dropna()
df_outcome.Rank = df_outcome.Rank.astype(int)
# df_outcome.set_index("Rank", inplace=True)  # Rank has duplicates
df_murmur = pd.read_html(murmur_url, flavor="bs4", header=[1], index_col=[0])[0].reset_index(drop=True).dropna()
df_murmur.Rank = df_murmur.Rank.astype(int)
# df_murmur.set_index("Rank", inplace=True)  # Rank has duplicates

from string import punctuation

team_name_pattern = f"""[\\w\\s{punctuation}]+"""
email_pattern = (
    f"""We processed an entry from Team (?P<team_name>{team_name_pattern}) """
    """for the Official phase of the George B\\. Moody PhysioNet Challenge 2022\\. """
    """This entry was submitted on (?P<submission_time>[\\d]{1,2}/[\\d]{1,2}/2022 [\\d]{1,2}:[\\d]{1,2}:[\\d]{1,2} ET) """
    f"""with ID (?P<submission_id>{team_name_pattern}_[\\d]{{1,5}}_[\\d]{{1,3}})\\.[\\n]+"""
    """We successfully evaluated your entry, which received the score (?P<outcome_cost>[\\d\\.]+) and """
    """(?P<murmur_weighted_accuracy>[\\d\\.]+) using the Challenge evaluation metric on the validation set\\. """
    """This entry was your team's (?P<submission_number>[\\d]{1,2})/10 entry for the Official phase\\."""
)
# usage:
# list(re.finditer(email_pattern, email_content))[0].groupdict()