2476 lines
765 KiB
Plaintext
2476 lines
765 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from itertools import combinations\n",
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"from cliffs_delta import cliffs_delta\n",
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"from scipy.stats import mannwhitneyu, kruskal, chi2_contingency"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 软件生态名\n",
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"ECO_NAMES = [\n",
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" # \"Apache\",\n",
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" # \"Jira\",\n",
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" # \"Mojang\",\n",
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" # \"MongoDB\",\n",
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" # \"Qt\",\n",
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" \"RedHat\",\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"PRO_ISSUE_DIR = Path(\"../data/processed/issues\")\n",
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"PRO_LINK_DIR = Path(\"../data/processed/links\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"RQ2_RESULT_DIR = Path(\"../data/rq2\")\n",
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"RQ2_RESULT_DIR.mkdir(parents=True, exist_ok=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Helper Functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_issue_df_of_type(issue_df: pd.DataFrame, link_df: pd.DataFrame, link_type: str):\n",
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" \"\"\"取出属于某类型链接的Issue DataFrame\"\"\"\n",
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"\n",
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" # 取出某类型链接及关联的Issue关键字\n",
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" link_df_of_type = link_df[link_df[\"link_type\"] == link_type]\n",
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" issue_keys_of_type = set(link_df_of_type[\"in_issue_key\"]).union(\n",
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" set(link_df_of_type[\"out_issue_key\"])\n",
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" )\n",
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"\n",
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" # 再取出属于该类型链接的Issue\n",
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" issue_df_of_type = issue_df[issue_df[\"key\"].isin(issue_keys_of_type)]\n",
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" return issue_df_of_type"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_wmw_stats(\n",
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" first_ser: pd.Series,\n",
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" second_ser: pd.Series,\n",
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" alternative: str = \"two-sided\", # 假设类型\n",
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"):\n",
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" \"\"\"执行WMW假设检验\"\"\"\n",
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"\n",
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" u_stat, p_value = mannwhitneyu(\n",
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" first_ser,\n",
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" second_ser,\n",
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" alternative=alternative,\n",
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" )\n",
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" return p_value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_cliff_delta(\n",
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" first_ser: pd.Series,\n",
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" second_ser: pd.Series,\n",
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"):\n",
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" \"\"\"计算Cliff Delta效应值\"\"\"\n",
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"\n",
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" delta, res = cliffs_delta(\n",
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" first_ser,\n",
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" second_ser,\n",
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" )\n",
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" return delta, res"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_kruskal_stats(\n",
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" df: pd.DataFrame,\n",
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" group_type: str,\n",
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" stat_type: str,\n",
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"):\n",
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" \"\"\"执行Kruskal-Wallis H假设检验\"\"\"\n",
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"\n",
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" # 为每个group_type准备数据\n",
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" data_groups = [group[stat_type].values for name, group in df.groupby(group_type)]\n",
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"\n",
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" # 使用Kruskal-Wallis H检验不同group_type的stat_type分布是否有显著差异\n",
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" stat, p_value = kruskal(*data_groups)\n",
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" return p_value"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 0.加载数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_df(eco_name: str):\n",
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" \"\"\"加载Issue和链接数据DataFrame\"\"\"\n",
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"\n",
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" issue_file = PRO_ISSUE_DIR / (eco_name + \".csv\")\n",
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" issue_df = pd.read_csv(\n",
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" issue_file,\n",
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" sep=\";\",\n",
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" parse_dates=[\"created_time\", \"closed_time\"],\n",
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" encoding=\"utf-8\",\n",
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" low_memory=False,\n",
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" )\n",
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"\n",
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" link_file = PRO_LINK_DIR / (eco_name + \".csv\")\n",
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" link_df = pd.read_csv(\n",
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" link_file,\n",
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" sep=\";\",\n",
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" parse_dates=[\n",
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" \"link_created_time\",\n",
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" \"created_time_in\",\n",
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" \"created_time_out\",\n",
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" \"closed_time_in\",\n",
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" \"closed_time_out\",\n",
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" ],\n",
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" encoding=\"utf-8\",\n",
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" low_memory=False,\n",
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" )\n",
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"\n",
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" return issue_df, link_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 待执行假设检验的类型对\n",
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"link_types = [\n",
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" \"Account\",\n",
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" \"Blocks\",\n",
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" \"Causality\",\n",
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" \"Cloners\",\n",
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" \"Depend\",\n",
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" \"Document\",\n",
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" \"Duplicate\",\n",
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" \"Epic\",\n",
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" \"Incorporates\",\n",
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" \"Issue split\",\n",
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" \"Related\",\n",
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" \"Subtask\",\n",
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" \"Triggers\",\n",
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"]\n",
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"\n",
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"hypo_test_types = list(combinations(link_types, 2))\n",
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"# hypo_test_types = [\n",
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"# (\"Epic\", \"Subtask\"),\n",
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"# (\"Cloners\", \"Duplicate\"),\n",
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"# (\"Blocks\", \"Depend\"),\n",
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"# (\"Causality\", \"Triggers\"),\n",
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"# (\"Related\", \"Blocks\"),\n",
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"# (\"Related\", \"Causality\"),\n",
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"# (\"Epic\", \"Blocks\"),\n",
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"# (\"Subtask\", \"Blocks\"),\n",
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"# (\"Epic\", \"Depend\"),\n",
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"# (\"Subtask\", \"Depend\"),\n",
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"# ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 定义特定时间刻度（秒）\n",
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"times_in_seconds = {\n",
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" \"1 minute\": 60,\n",
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" \"1 hour\": 3600,\n",
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" \"1 day\": 86400,\n",
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" \"1 month\": 86400 * 30,\n",
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" \"1 year\": 86400 * 365,\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1.基本信息"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def add_link_scope(link_df: pd.DataFrame):\n",
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" \"\"\"由链接两端Issue所属项目关键字确定链接范围\"\"\"\n",
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"\n",
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" link_df[\"scope\"] = np.where(\n",
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" link_df[\"project_key_in\"] == link_df[\"project_key_out\"],\n",
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" \"in\",\n",
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" \"cross\",\n",
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" )\n",
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"\n",
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" return link_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_overview(eco_name: str, link_df: pd.DataFrame):\n",
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" \"\"\"统计不同类型链接的基本信息\"\"\"\n",
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"\n",
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" num_links = len(link_df) # 链接总数\n",
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"\n",
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" # 统计不同类型链接数量\n",
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" link_types_overview_df = (\n",
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" link_df[\"link_type\"]\n",
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" .value_counts()\n",
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" .rename_axis(\"link_type\")\n",
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" .reset_index(name=\"number\")\n",
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" )\n",
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"\n",
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" # 插入软件生态名列\n",
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" link_types_overview_df.insert(0, \"ecosystem\", eco_name)\n",
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"\n",
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" # 计算不同类别链接比例\n",
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" link_types_overview_df[\"percentage\"] = link_types_overview_df[\"number\"].apply(\n",
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" lambda x: round(x / num_links * 100, 2)\n",
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" )\n",
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"\n",
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" # 统计不同类别链接的范围分布及比例\n",
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" scopes = link_df[\"scope\"].unique()\n",
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" for scope in scopes:\n",
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" link_types_overview_df[\"num_\" + scope] = link_types_overview_df[\n",
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" \"link_type\"\n",
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" ].apply(lambda x: sum(link_df[link_df[\"link_type\"] == x][\"scope\"] == scope))\n",
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"\n",
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" link_types_overview_df[\"per_\" + scope] = (\n",
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" link_types_overview_df[\"num_\" + scope] / link_types_overview_df[\"number\"]\n",
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" ).apply(lambda x: round(x * 100, 2))\n",
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"\n",
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" # 绘图\n",
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" # 获取链接类型与数量\n",
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" link_types = link_types_overview_df[\"link_type\"]\n",
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" numbers = link_types_overview_df[\"number\"]\n",
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"\n",
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" # 确定哪些类型的比例小于2%\n",
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" small_idx = [idx for idx, number in enumerate(numbers) if number / num_links < 0.02]\n",
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" large_idx = [\n",
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" idx for idx, number in enumerate(numbers) if number / num_links >= 0.02\n",
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" ]\n",
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"\n",
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" # 将<2%的类型归类为“Other”\n",
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" # 计算Other类型链接数量\n",
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" other_numbers = sum(numbers.iloc[small_idx])\n",
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" # 取出>=2%的类型名和数量\n",
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" plot_types = link_types.iloc[large_idx]\n",
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" plot_numbers = numbers.iloc[large_idx]\n",
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"\n",
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" # 把Other类型和数量添加至待绘制类型\n",
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" if small_idx:\n",
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" plot_types = pd.concat([plot_types, pd.Series([\"Other\"])], ignore_index=True)\n",
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" plot_numbers = pd.concat(\n",
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" [plot_numbers, pd.Series(other_numbers)], ignore_index=True\n",
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" )\n",
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"\n",
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" # 首先绘制链接类型分布饼图\n",
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" fig1, ax1 = plt.subplots(figsize=(8, 8))\n",
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" # 生成颜色，'viridis'可以替换为 'plasma', 'inferno', 'magma', 'cividis' 等\n",
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" cmap = plt.get_cmap(\"plasma\")\n",
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" colors = cmap(np.linspace(0, 1, len(plot_types)))\n",
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" ax1.pie(\n",
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" plot_numbers,\n",
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" labels=plot_types,\n",
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" colors=colors,\n",
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" autopct=lambda pct: f\"{pct:.2f}%\" if pct >= 2 else \"\",\n",
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" startangle=140,\n",
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" )\n",
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"\n",
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" # plt.legend(loc=\"upper right\", fontsize=18)\n",
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" ax1.set_title(\"Link types distribution\", fontsize=20)\n",
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" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
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" # 保存为PDF\n",
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" plt.savefig(RQ2_RESULT_DIR / (\"link_types_dist_pie.pdf\"), format=\"pdf\")\n",
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"\n",
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" # 其次绘制堆叠条形图\n",
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" fig2, ax2 = plt.subplots(figsize=(18, 9))\n",
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"\n",
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" # 堆叠条形图底部开始的位置\n",
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" bottoms = np.zeros(len(link_types))\n",
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"\n",
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" for scope in scopes:\n",
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" # 获取各类型链接在当前范围下的数量\n",
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" num_scope_links = link_types_overview_df[\"num_\" + scope]\n",
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"\n",
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" # 绘制条形图\n",
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" ax2.bar(link_types, num_scope_links, bottom=bottoms, label=scope)\n",
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"\n",
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" # 更新下一段条形图的底部开始位置\n",
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" bottoms += num_scope_links\n",
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"\n",
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" # 为每个柱子添加文本信息\n",
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" percentages = link_types_overview_df[\"percentage\"]\n",
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"\n",
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" for idx, (number, percentage) in enumerate(zip(numbers, percentages)):\n",
|
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" text = f\"{number} ({percentage}%)\"\n",
|
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" ax2.text(idx, number, text, ha=\"center\", va=\"bottom\")\n",
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"\n",
|
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" ax2.set_ylabel(\"Number (log)\")\n",
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" ax2.set_title(\"Link types distribution\")\n",
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" ax2.set_yscale(\"log\") # 应用对数尺度\n",
|
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" ax2.legend()\n",
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"\n",
|
||
" # 为了让 x 轴标签更清晰，可以将它们旋转\n",
|
||
" plt.xticks(rotation=45, ha=\"right\")\n",
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"\n",
|
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" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
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" # 保存为PDF\n",
|
||
" plt.savefig(RQ2_RESULT_DIR / (\"link_types_dist.pdf\"), format=\"pdf\")\n",
|
||
"\n",
|
||
" return link_types_overview_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 基本信息\n",
|
||
"link_types_overview = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2.Issue创建、链接建立时间差"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_time_interval(eco_name: str, link_df: pd.DataFrame):\n",
|
||
" \"\"\"统计分析两类时间差\"\"\"\n",
|
||
"\n",
|
||
" def get_time_stats(link_type: str, interval_type: str, df: pd.DataFrame):\n",
|
||
"\n",
|
||
" # 取出对应类型的链接\n",
|
||
" df = df[df[\"link_type\"] == link_type]\n",
|
||
"\n",
|
||
" # 计算各个统计指标（天数）\n",
|
||
" min_val = round(df[interval_type].min() / pd.Timedelta(days=1), 1)\n",
|
||
" # 1/4分位数\n",
|
||
" q1_val = round(df[interval_type].quantile(0.25) / pd.Timedelta(days=1), 1)\n",
|
||
" median_val = round(df[interval_type].median() / pd.Timedelta(days=1), 1)\n",
|
||
" # 3/4分位数\n",
|
||
" q3_val = round(df[interval_type].quantile(0.75) / pd.Timedelta(days=1), 1)\n",
|
||
" max_val = round(df[interval_type].max() / pd.Timedelta(days=1), 1)\n",
|
||
" mean_val = round(df[interval_type].mean() / pd.Timedelta(days=1), 1)\n",
|
||
" std_val = round(df[interval_type].std() / pd.Timedelta(days=1), 1)\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" link_type,\n",
|
||
" interval_type,\n",
|
||
" min_val,\n",
|
||
" q1_val,\n",
|
||
" median_val,\n",
|
||
" q3_val,\n",
|
||
" max_val,\n",
|
||
" mean_val,\n",
|
||
" std_val,\n",
|
||
" )\n",
|
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"\n",
|
||
" # 裁切出使用到的列\n",
|
||
" link_df = link_df[\n",
|
||
" [\n",
|
||
" \"link_type\",\n",
|
||
" \"in_issue_key\",\n",
|
||
" \"out_issue_key\",\n",
|
||
" \"link_created_time\",\n",
|
||
" \"created_time_in\",\n",
|
||
" \"created_time_out\",\n",
|
||
" \"scope\",\n",
|
||
" ]\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # 删除掉link_created_time、created_time_in、created_time_out为空的行\n",
|
||
" link_df = link_df.dropna(\n",
|
||
" subset=[\"link_created_time\", \"created_time_in\", \"created_time_out\"], how=\"any\"\n",
|
||
" )\n",
|
||
" print(\n",
|
||
" f\"✔ {len(link_df)} links after removing links with null link/issue created time\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 创建CTI、LTI两列\n",
|
||
" link_df[\"cti\"] = (link_df[\"created_time_in\"] - link_df[\"created_time_out\"]).abs()\n",
|
||
" max_created_time = link_df[[\"created_time_in\", \"created_time_out\"]].max(axis=1)\n",
|
||
" link_df[\"lti\"] = link_df[\"link_created_time\"] - max_created_time\n",
|
||
" # !注意：有些行的LTI值为负，这是因为获取link_created_time时出错，直接删除对应行\n",
|
||
" link_df = link_df[link_df[\"lti\"] >= pd.Timedelta(0)]\n",
|
||
" print(f\"✔ {len(link_df)} links after removing links with negative LTI\")\n",
|
||
"\n",
|
||
" # 统计CTI、LTI两列\n",
|
||
" time_interval_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"link_type\", # 链接类型\n",
|
||
" \"interval_type\", # 时间间隔类型\n",
|
||
" \"min\", # 最小值\n",
|
||
" \"q1\", # 1/4分位数\n",
|
||
" \"median\", # 中值\n",
|
||
" \"q3\", # 3/4分位数\n",
|
||
" \"max\", # 最大值\n",
|
||
" \"mean\", # 平均值\n",
|
||
" \"std\", # 标准差\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" for link_type in link_df[\"link_type\"].unique():\n",
|
||
" for interval_type in [\"cti\", \"lti\"]:\n",
|
||
" time_interval_df.loc[len(time_interval_df)] = get_time_stats(\n",
|
||
" link_type, interval_type, link_df\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 执行假设检验\n",
|
||
" for interval_type in [\"cti\", \"lti\"]:\n",
|
||
" statistic_type = interval_type + \"_secs\"\n",
|
||
" # statistic_type = interval_type + \"_days\"\n",
|
||
"\n",
|
||
" # offset = 0 # 偏移量，避免对零值取对数\n",
|
||
" # 转换 timedelta 为秒数\n",
|
||
" link_df[statistic_type] = link_df[interval_type].dt.total_seconds()\n",
|
||
" # # 转换 timedelta 为天数\n",
|
||
" # link_df[statistic_type] = (\n",
|
||
" # link_df[interval_type] / pd.Timedelta(days=1)\n",
|
||
" # )\n",
|
||
"\n",
|
||
" # 执行Kruskal-Wallis H假设检验\n",
|
||
" p_value_kru = get_kruskal_stats(link_df, \"link_type\", statistic_type)\n",
|
||
" print(f\"{interval_type.upper()}\")\n",
|
||
" print(f\"Kruskal-Wallis H, p_value: {p_value_kru:.5f}\")\n",
|
||
"\n",
|
||
" # 绘制不同link_type对应的时间间隔分布\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(20, 8))\n",
|
||
"\n",
|
||
" # # 绘制小提琴图\n",
|
||
" # sns.violinplot(\n",
|
||
" # data=link_df,\n",
|
||
" # x=\"link_type\",\n",
|
||
" # y=statistic_type,\n",
|
||
" # inner=\"quartile\",\n",
|
||
" # color=\"0.8\",\n",
|
||
" # scale=\"width\",\n",
|
||
" # )\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(data=link_df, x=\"link_type\", y=statistic_type)\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(f\"{interval_type.upper()} dist. of different Link types\", fontsize=20)\n",
|
||
" plt.xticks(rotation=45) # 将x轴标签旋转45度，以防止文字重叠\n",
|
||
" plt.xlabel(\"Link type\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{interval_type.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" # 添加特定时间点的标注\n",
|
||
" plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" file_name = f\"{interval_type}_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ2_RESULT_DIR / file_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 执行WMW假设检验\n",
|
||
" for first_type, second_type in hypo_test_types:\n",
|
||
" first_df = link_df[link_df[\"link_type\"] == first_type]\n",
|
||
" second_df = link_df[link_df[\"link_type\"] == second_type]\n",
|
||
"\n",
|
||
" first_ser = first_df[statistic_type]\n",
|
||
" second_ser = second_df[statistic_type]\n",
|
||
"\n",
|
||
" p_value_ne = get_wmw_stats(first_ser, second_ser)\n",
|
||
" p_value_le = get_wmw_stats(first_ser, second_ser, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_ser, second_ser, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_ser, second_ser)\n",
|
||
"\n",
|
||
" print(\n",
|
||
" f\"alternative: {first_type} != {second_type}, p_value: {p_value_ne:.5f}\"\n",
|
||
" )\n",
|
||
" print(\n",
|
||
" f\"alternative: {first_type} < {second_type}, p_value: {p_value_le:.5f}\"\n",
|
||
" )\n",
|
||
" print(\n",
|
||
" f\"alternative: {first_type} > {second_type}, p_value: {p_value_ge:.5f}\"\n",
|
||
" )\n",
|
||
" print(f\"{first_type} & {second_type}, delta: {delta:.5f}, {res}\")\n",
|
||
"\n",
|
||
" # # 绘制并保存对比小提琴图\n",
|
||
" # combined_df = pd.concat([first_df, second_df])\n",
|
||
" # sns.set_theme(style=\"whitegrid\")\n",
|
||
" # plt.figure(figsize=(10, 6))\n",
|
||
" # sns.violinplot(\n",
|
||
" # data=combined_df,\n",
|
||
" # x=\"link_type\",\n",
|
||
" # y=(statistic_type),\n",
|
||
" # scale=\"width\",\n",
|
||
" # )\n",
|
||
" # plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # # 添加标题和轴标签\n",
|
||
" # plt.title(\n",
|
||
" # f\"{interval_type.upper()} dist. of {first_type} and {second_type}\"\n",
|
||
" # )\n",
|
||
" # plt.xlabel(\"Link type\")\n",
|
||
" # plt.ylabel(f\"{interval_type.upper()} (log)\")\n",
|
||
"\n",
|
||
" # # 添加特定时间点的标注\n",
|
||
" # plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" # plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # # 保存为PDF\n",
|
||
" # file_name = f\"{interval_type}_{first_type}_{second_type}_dist.pdf\"\n",
|
||
" # plt.savefig(\n",
|
||
" # RQ2_RESULT_DIR / file_name,\n",
|
||
" # format=\"pdf\",\n",
|
||
" # )\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return time_interval_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"time_interval = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3.讨论规模"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_comment_scale(eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame):\n",
|
||
" \"\"\"统计不同类型链接的评论数量\"\"\"\n",
|
||
"\n",
|
||
" def get_stats(link_type: str, stat_type: str, combined_df: pd.DataFrame):\n",
|
||
" \"\"\"计算各个统计指标\"\"\"\n",
|
||
"\n",
|
||
" df = get_issue_df_of_type(issue_df, link_df, link_type).copy()\n",
|
||
"\n",
|
||
" min_val = round(df[stat_type].min(), 1)\n",
|
||
" q1_val = round(df[stat_type].quantile(0.25), 1)\n",
|
||
" median_val = round(df[stat_type].median(), 1)\n",
|
||
" q3_val = round(df[stat_type].quantile(0.75), 1)\n",
|
||
" max_val = round(df[stat_type].max(), 1)\n",
|
||
" mean_val = round(df[stat_type].mean(), 1)\n",
|
||
" std_val = round(df[stat_type].std(), 1)\n",
|
||
"\n",
|
||
" # 给当前类型链接的Issue添加链接类型信息并合并保存\n",
|
||
" df[\"link_type\"] = link_type\n",
|
||
" combined_df = pd.concat([combined_df, df])\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" link_type,\n",
|
||
" min_val,\n",
|
||
" q1_val,\n",
|
||
" median_val,\n",
|
||
" q3_val,\n",
|
||
" max_val,\n",
|
||
" mean_val,\n",
|
||
" std_val,\n",
|
||
" combined_df,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 统计评论数量\n",
|
||
" comment_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"link_type\", # 链接类型\n",
|
||
" \"min\", # 最小值\n",
|
||
" \"q1\", # 1/4分位数\n",
|
||
" \"median\", # 中值\n",
|
||
" \"q3\", # 3/4分位数\n",
|
||
" \"max\", # 最大值\n",
|
||
" \"mean\", # 平均值\n",
|
||
" \"std\", # 标准差\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" statistic_type = \"num_comments\"\n",
|
||
" combined_issue_df = pd.DataFrame()\n",
|
||
"\n",
|
||
" for link_type in link_df[\"link_type\"].unique():\n",
|
||
" result = get_stats(link_type, statistic_type, combined_issue_df)\n",
|
||
" comment_df.loc[len(comment_df)] = result[0:-1]\n",
|
||
" combined_issue_df = result[-1]\n",
|
||
"\n",
|
||
" # 执行Kruskal-Wallis H假设检验\n",
|
||
" p_value_kru = get_kruskal_stats(combined_issue_df, \"link_type\", statistic_type)\n",
|
||
" print(f\"{statistic_type.upper()}\")\n",
|
||
" print(f\"Kruskal-Wallis H, p_value: {p_value_kru:.5f}\")\n",
|
||
"\n",
|
||
" # 绘制不同link_type对应的讨论规模分布\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(20, 8))\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(data=combined_issue_df, x=\"link_type\", y=statistic_type)\n",
|
||
" # plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(f\"{statistic_type.upper()} dist. of different Link types\", fontsize=20)\n",
|
||
" plt.xticks(rotation=45) # 将x轴标签旋转45度，以防止文字重叠\n",
|
||
" plt.xlabel(\"Link type\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{statistic_type.upper()}\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" file_name = f\"{statistic_type}_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ2_RESULT_DIR / file_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 执行WMW假设检验\n",
|
||
" for first_type, second_type in hypo_test_types:\n",
|
||
" first_df = get_issue_df_of_type(issue_df, link_df, first_type)\n",
|
||
" second_df = get_issue_df_of_type(issue_df, link_df, second_type)\n",
|
||
"\n",
|
||
" first_ser = first_df[statistic_type]\n",
|
||
" second_ser = second_df[statistic_type]\n",
|
||
"\n",
|
||
" p_value_ne = get_wmw_stats(first_ser, second_ser)\n",
|
||
" p_value_le = get_wmw_stats(first_ser, second_ser, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_ser, second_ser, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_ser, second_ser)\n",
|
||
"\n",
|
||
" print(f\"{statistic_type.upper()}\")\n",
|
||
" print(f\"alternative: {first_type} != {second_type}, p_value: {p_value_ne:.5f}\")\n",
|
||
" print(f\"alternative: {first_type} < {second_type}, p_value: {p_value_le:.5f}\")\n",
|
||
" print(f\"alternative: {first_type} > {second_type}, p_value: {p_value_ge:.5f}\")\n",
|
||
" print(f\"{first_type} & {second_type}, delta: {delta:.5f}, {res}\")\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return comment_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"comment_scale = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.解决比例"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_solved_proportion(eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame):\n",
|
||
" \"\"\"统计不同类型链接的解决比例\"\"\"\n",
|
||
"\n",
|
||
" def get_sloved_stats(link_type: str, stat_type: str, combined_df: pd.DataFrame):\n",
|
||
" \"\"\"统计Issue解决比例\"\"\"\n",
|
||
"\n",
|
||
" df = get_issue_df_of_type(issue_df, link_df, link_type).copy()\n",
|
||
"\n",
|
||
" # 当前类型关联的Issue总数\n",
|
||
" num_issues = len(df)\n",
|
||
" # 当前类型关联的非Closed Issue总数\n",
|
||
" num_not_closed = (df[stat_type] != \"Closed\").sum()\n",
|
||
" # 当前类型关联的非Closed Issue比例\n",
|
||
" per_not_closed = round(num_not_closed / num_issues * 100, 2)\n",
|
||
" # 当前类型关联的Closed Issue总数\n",
|
||
" num_closed = (df[stat_type] == \"Closed\").sum()\n",
|
||
" # 当前类型关联的Closed Issue比例\n",
|
||
" per_closed = round(num_closed / num_issues * 100, 2)\n",
|
||
"\n",
|
||
" # 给当前类型链接的Issue添加链接类型信息并合并保存\n",
|
||
" df[\"link_type\"] = link_type\n",
|
||
" combined_df = pd.concat([combined_df, df])\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" link_type,\n",
|
||
" num_issues,\n",
|
||
" num_not_closed,\n",
|
||
" per_not_closed,\n",
|
||
" num_closed,\n",
|
||
" per_closed,\n",
|
||
" combined_df,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 统计Issue解决比例\n",
|
||
" solved_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"link_type\", # 链接类型\n",
|
||
" \"num_issues\", # 该类型关联的Issue总数\n",
|
||
" \"num_not_closed\", # 该类型关联的非Closed Issue总数\n",
|
||
" \"per_not_closed\", # 该类型关联的非Closed Issue比例\n",
|
||
" \"num_closed\", # 该类型关联的Closed Issue总数\n",
|
||
" \"per_closed\", # 该类型关联的Closed Issue比例\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" statistic_type = \"status\"\n",
|
||
" combined_issue_df = pd.DataFrame()\n",
|
||
" for link_type in link_df[\"link_type\"].unique():\n",
|
||
" result = get_sloved_stats(link_type, statistic_type, combined_issue_df)\n",
|
||
" solved_df.loc[len(solved_df)] = result[0:-1]\n",
|
||
" combined_issue_df = result[-1]\n",
|
||
"\n",
|
||
" # 检验不同链接类型的Issue的解决比例是否有差异\n",
|
||
" # 创建列联表\n",
|
||
" table = pd.crosstab(\n",
|
||
" combined_issue_df[\"link_type\"], combined_issue_df[statistic_type] == \"Closed\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 执行卡方检验\n",
|
||
" chi2, p, dof, expected = chi2_contingency(table)\n",
|
||
" print(\"Solved Proportion:\")\n",
|
||
" print(f\"X^2: {chi2:.5f} p: {p:.5f}\")\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return solved_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"solved_proportion = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5.解决时长"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_sti(eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame):\n",
|
||
" \"\"\"统计不同类型链接的解决时长\"\"\"\n",
|
||
"\n",
|
||
" def get_filtered_df(df: pd.DataFrame):\n",
|
||
" \"\"\"选出状态为Closed、且STI非空的Issue\"\"\"\n",
|
||
"\n",
|
||
" return df[(df[\"status\"] == \"Closed\") & (df[\"sti\"].notna())]\n",
|
||
"\n",
|
||
" def get_time_stats(link_type: str, interval_type: str, combined_df: pd.DataFrame):\n",
|
||
"\n",
|
||
" df = get_issue_df_of_type(issue_df, link_df, link_type)\n",
|
||
"\n",
|
||
" # 选出STI非空的关闭Issue\n",
|
||
" df = get_filtered_df(df).copy()\n",
|
||
"\n",
|
||
" # 计算各个统计指标（天数）\n",
|
||
" min_val = round(df[interval_type].min() / pd.Timedelta(days=1), 1)\n",
|
||
" # 1/4分位数\n",
|
||
" q1_val = round(df[interval_type].quantile(0.25) / pd.Timedelta(days=1), 1)\n",
|
||
" median_val = round(df[interval_type].median() / pd.Timedelta(days=1), 1)\n",
|
||
" # 3/4分位数\n",
|
||
" q3_val = round(df[interval_type].quantile(0.75) / pd.Timedelta(days=1), 1)\n",
|
||
" max_val = round(df[interval_type].max() / pd.Timedelta(days=1), 1)\n",
|
||
" mean_val = round(df[interval_type].mean() / pd.Timedelta(days=1), 1)\n",
|
||
" std_val = round(df[interval_type].std() / pd.Timedelta(days=1), 1)\n",
|
||
"\n",
|
||
" # 给当前类型链接的Issue添加链接类型信息并合并保存\n",
|
||
" df[\"link_type\"] = link_type\n",
|
||
" combined_df = pd.concat([combined_df, df])\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" link_type,\n",
|
||
" min_val,\n",
|
||
" q1_val,\n",
|
||
" median_val,\n",
|
||
" q3_val,\n",
|
||
" max_val,\n",
|
||
" mean_val,\n",
|
||
" std_val,\n",
|
||
" combined_df,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 创建STI（解决时长）列：关闭时间-创建时间\n",
|
||
" interval_type = \"sti\"\n",
|
||
" issue_df[interval_type] = issue_df[\"closed_time\"] - issue_df[\"created_time\"]\n",
|
||
" # 把timedelta转换为秒数，方便进行假设检验与绘图\n",
|
||
" statistic_type = interval_type + \"_secs\"\n",
|
||
" issue_df[statistic_type] = issue_df[interval_type].dt.total_seconds()\n",
|
||
"\n",
|
||
" # 统计解决时长\n",
|
||
" sti_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"link_type\", # 链接类型\n",
|
||
" \"min\", # 最小值\n",
|
||
" \"q1\", # 1/4分位数\n",
|
||
" \"median\", # 中值\n",
|
||
" \"q3\", # 3/4分位数\n",
|
||
" \"max\", # 最大值\n",
|
||
" \"mean\", # 平均值\n",
|
||
" \"std\", # 标准差\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" combined_issue_df = pd.DataFrame()\n",
|
||
"\n",
|
||
" for link_type in link_df[\"link_type\"].unique():\n",
|
||
" result = get_time_stats(link_type, interval_type, combined_issue_df)\n",
|
||
" sti_df.loc[len(sti_df)] = result[0:-1]\n",
|
||
" combined_issue_df = result[-1]\n",
|
||
"\n",
|
||
" # 执行Kruskal-Wallis H假设检验\n",
|
||
" p_value_kru = get_kruskal_stats(combined_issue_df, \"link_type\", statistic_type)\n",
|
||
" print(f\"{interval_type.upper()}\")\n",
|
||
" print(f\"Kruskal-Wallis H, p_value: {p_value_kru:.5f}\")\n",
|
||
"\n",
|
||
" # 绘制不同link_type对应的时间间隔分布\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(20, 8))\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(data=combined_issue_df, x=\"link_type\", y=statistic_type)\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(f\"{interval_type.upper()} dist. of different Link types\", fontsize=20)\n",
|
||
" plt.xticks(rotation=45) # 将x轴标签旋转45度，以防止文字重叠\n",
|
||
" plt.xlabel(\"Link type\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{interval_type.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" # 添加特定时间点的标注\n",
|
||
" plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" file_name = f\"{interval_type}_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ2_RESULT_DIR / file_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 执行WMW假设检验\n",
|
||
" for first_type, second_type in hypo_test_types:\n",
|
||
" first_df = get_issue_df_of_type(issue_df, link_df, first_type)\n",
|
||
" second_df = get_issue_df_of_type(issue_df, link_df, second_type)\n",
|
||
" first_df = get_filtered_df(first_df)\n",
|
||
" second_df = get_filtered_df(second_df)\n",
|
||
"\n",
|
||
" first_ser = first_df[statistic_type]\n",
|
||
" second_ser = second_df[statistic_type]\n",
|
||
"\n",
|
||
" p_value_ne = get_wmw_stats(first_ser, second_ser)\n",
|
||
" p_value_le = get_wmw_stats(first_ser, second_ser, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_ser, second_ser, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_ser, second_ser)\n",
|
||
"\n",
|
||
" print(f\"alternative: {first_type} != {second_type}, p_value: {p_value_ne:.5f}\")\n",
|
||
" print(f\"alternative: {first_type} < {second_type}, p_value: {p_value_le:.5f}\")\n",
|
||
" print(f\"alternative: {first_type} > {second_type}, p_value: {p_value_ge:.5f}\")\n",
|
||
" print(f\"{first_type} & {second_type}, delta: {delta:.5f}, {res}\")\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return sti_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"sti = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✔ 222681 links after removing links with null link/issue created time\n",
|
||
"✔ 222483 links after removing links with negative LTI\n",
|
||
"CTI\n",
|
||
"Kruskal-Wallis H, p_value: 0.00000\n",
|
||
"alternative: Account != Blocks, p_value: 0.00115\n",
|
||
"alternative: Account < Blocks, p_value: 0.99943\n",
|
||
"alternative: Account > Blocks, p_value: 0.00057\n",
|
||
"Account & Blocks, delta: 0.08184, negligible\n",
|
||
"alternative: Account != Causality, p_value: 0.00000\n",
|
||
"alternative: Account < Causality, p_value: 0.00000\n",
|
||
"alternative: Account > Causality, p_value: 1.00000\n",
|
||
"Account & Causality, delta: -0.12418, negligible\n",
|
||
"alternative: Account != Cloners, p_value: 0.00000\n",
|
||
"alternative: Account < Cloners, p_value: 1.00000\n",
|
||
"alternative: Account > Cloners, p_value: 0.00000\n",
|
||
"Account & Cloners, delta: 0.20326, small\n",
|
||
"alternative: Account != Depend, p_value: 0.00000\n",
|
||
"alternative: Account < Depend, p_value: 1.00000\n",
|
||
"alternative: Account > Depend, p_value: 0.00000\n",
|
||
"Account & Depend, delta: 0.14260, negligible\n",
|
||
"alternative: Account != Document, p_value: 0.00000\n",
|
||
"alternative: Account < Document, p_value: 0.00000\n",
|
||
"alternative: Account > Document, p_value: 1.00000\n",
|
||
"Account & Document, delta: -0.15533, small\n",
|
||
"alternative: Account != Duplicate, p_value: 0.01483\n",
|
||
"alternative: Account < Duplicate, p_value: 0.00742\n",
|
||
"alternative: Account > Duplicate, p_value: 0.99258\n",
|
||
"Account & Duplicate, delta: -0.06287, negligible\n",
|
||
"alternative: Account != Epic, p_value: 0.31862\n",
|
||
"alternative: Account < Epic, p_value: 0.15931\n",
|
||
"alternative: Account > Epic, p_value: 0.84069\n",
|
||
"Account & Epic, delta: -0.02488, negligible\n",
|
||
"alternative: Account != Incorporates, p_value: 0.74879\n",
|
||
"alternative: Account < Incorporates, p_value: 0.62561\n",
|
||
"alternative: Account > Incorporates, p_value: 0.37440\n",
|
||
"Account & Incorporates, delta: 0.00813, negligible\n",
|
||
"alternative: Account != Issue split, p_value: 0.07474\n",
|
||
"alternative: Account < Issue split, p_value: 0.03737\n",
|
||
"alternative: Account > Issue split, p_value: 0.96264\n",
|
||
"Account & Issue split, delta: -0.05957, negligible\n",
|
||
"alternative: Account != Related, p_value: 0.00000\n",
|
||
"alternative: Account < Related, p_value: 0.00000\n",
|
||
"alternative: Account > Related, p_value: 1.00000\n",
|
||
"Account & Related, delta: -0.13166, negligible\n",
|
||
"alternative: Account != Subtask, p_value: 0.00000\n",
|
||
"alternative: Account < Subtask, p_value: 1.00000\n",
|
||
"alternative: Account > Subtask, p_value: 0.00000\n",
|
||
"Account & Subtask, delta: 0.37537, medium\n",
|
||
"alternative: Account != Triggers, p_value: 0.01668\n",
|
||
"alternative: Account < Triggers, p_value: 0.00834\n",
|
||
"alternative: Account > Triggers, p_value: 0.99167\n",
|
||
"Account & Triggers, delta: -0.08864, negligible\n",
|
||
"alternative: Blocks != Causality, p_value: 0.00000\n",
|
||
"alternative: Blocks < Causality, p_value: 0.00000\n",
|
||
"alternative: Blocks > Causality, p_value: 1.00000\n",
|
||
"Blocks & Causality, delta: -0.19291, small\n",
|
||
"alternative: Blocks != Cloners, p_value: 0.00000\n",
|
||
"alternative: Blocks < Cloners, p_value: 1.00000\n",
|
||
"alternative: Blocks > Cloners, p_value: 0.00000\n",
|
||
"Blocks & Cloners, delta: 0.12813, negligible\n",
|
||
"alternative: Blocks != Depend, p_value: 0.00000\n",
|
||
"alternative: Blocks < Depend, p_value: 1.00000\n",
|
||
"alternative: Blocks > Depend, p_value: 0.00000\n",
|
||
"Blocks & Depend, delta: 0.06652, negligible\n",
|
||
"alternative: Blocks != Document, p_value: 0.00000\n",
|
||
"alternative: Blocks < Document, p_value: 0.00000\n",
|
||
"alternative: Blocks > Document, p_value: 1.00000\n",
|
||
"Blocks & Document, delta: -0.20596, small\n",
|
||
"alternative: Blocks != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Blocks < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Blocks > Duplicate, p_value: 1.00000\n",
|
||
"Blocks & Duplicate, delta: -0.13841, negligible\n",
|
||
"alternative: Blocks != Epic, p_value: 0.00000\n",
|
||
"alternative: Blocks < Epic, p_value: 0.00000\n",
|
||
"alternative: Blocks > Epic, p_value: 1.00000\n",
|
||
"Blocks & Epic, delta: -0.08102, negligible\n",
|
||
"alternative: Blocks != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Blocks < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Blocks > Incorporates, p_value: 1.00000\n",
|
||
"Blocks & Incorporates, delta: -0.07729, negligible\n",
|
||
"alternative: Blocks != Issue split, p_value: 0.00000\n",
|
||
"alternative: Blocks < Issue split, p_value: 0.00000\n",
|
||
"alternative: Blocks > Issue split, p_value: 1.00000\n",
|
||
"Blocks & Issue split, delta: -0.13418, negligible\n",
|
||
"alternative: Blocks != Related, p_value: 0.00000\n",
|
||
"alternative: Blocks < Related, p_value: 0.00000\n",
|
||
"alternative: Blocks > Related, p_value: 1.00000\n",
|
||
"Blocks & Related, delta: -0.19573, small\n",
|
||
"alternative: Blocks != Subtask, p_value: 0.00000\n",
|
||
"alternative: Blocks < Subtask, p_value: 1.00000\n",
|
||
"alternative: Blocks > Subtask, p_value: 0.00000\n",
|
||
"Blocks & Subtask, delta: 0.31458, small\n",
|
||
"alternative: Blocks != Triggers, p_value: 0.00000\n",
|
||
"alternative: Blocks < Triggers, p_value: 0.00000\n",
|
||
"alternative: Blocks > Triggers, p_value: 1.00000\n",
|
||
"Blocks & Triggers, delta: -0.16263, small\n",
|
||
"alternative: Causality != Cloners, p_value: 0.00000\n",
|
||
"alternative: Causality < Cloners, p_value: 1.00000\n",
|
||
"alternative: Causality > Cloners, p_value: 0.00000\n",
|
||
"Causality & Cloners, delta: 0.30456, small\n",
|
||
"alternative: Causality != Depend, p_value: 0.00000\n",
|
||
"alternative: Causality < Depend, p_value: 1.00000\n",
|
||
"alternative: Causality > Depend, p_value: 0.00000\n",
|
||
"Causality & Depend, delta: 0.25200, small\n",
|
||
"alternative: Causality != Document, p_value: 0.44622\n",
|
||
"alternative: Causality < Document, p_value: 0.22311\n",
|
||
"alternative: Causality > Document, p_value: 0.77690\n",
|
||
"Causality & Document, delta: -0.01333, negligible\n",
|
||
"alternative: Causality != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Causality < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Causality > Duplicate, p_value: 0.00000\n",
|
||
"Causality & Duplicate, delta: 0.05356, negligible\n",
|
||
"alternative: Causality != Epic, p_value: 0.00000\n",
|
||
"alternative: Causality < Epic, p_value: 1.00000\n",
|
||
"alternative: Causality > Epic, p_value: 0.00000\n",
|
||
"Causality & Epic, delta: 0.10076, negligible\n",
|
||
"alternative: Causality != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Causality < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Causality > Incorporates, p_value: 0.00000\n",
|
||
"Causality & Incorporates, delta: 0.12547, negligible\n",
|
||
"alternative: Causality != Issue split, p_value: 0.00382\n",
|
||
"alternative: Causality < Issue split, p_value: 0.99809\n",
|
||
"alternative: Causality > Issue split, p_value: 0.00191\n",
|
||
"Causality & Issue split, delta: 0.06968, negligible\n",
|
||
"alternative: Causality != Related, p_value: 0.42593\n",
|
||
"alternative: Causality < Related, p_value: 0.21296\n",
|
||
"alternative: Causality > Related, p_value: 0.78704\n",
|
||
"Causality & Related, delta: -0.00752, negligible\n",
|
||
"alternative: Causality != Subtask, p_value: 0.00000\n",
|
||
"alternative: Causality < Subtask, p_value: 1.00000\n",
|
||
"alternative: Causality > Subtask, p_value: 0.00000\n",
|
||
"Causality & Subtask, delta: 0.45532, medium\n",
|
||
"alternative: Causality != Triggers, p_value: 0.15053\n",
|
||
"alternative: Causality < Triggers, p_value: 0.92474\n",
|
||
"alternative: Causality > Triggers, p_value: 0.07527\n",
|
||
"Causality & Triggers, delta: 0.04152, negligible\n",
|
||
"alternative: Cloners != Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners < Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners > Depend, p_value: 1.00000\n",
|
||
"Cloners & Depend, delta: -0.06660, negligible\n",
|
||
"alternative: Cloners != Document, p_value: 0.00000\n",
|
||
"alternative: Cloners < Document, p_value: 0.00000\n",
|
||
"alternative: Cloners > Document, p_value: 1.00000\n",
|
||
"Cloners & Document, delta: -0.31021, small\n",
|
||
"alternative: Cloners != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners > Duplicate, p_value: 1.00000\n",
|
||
"Cloners & Duplicate, delta: -0.25172, small\n",
|
||
"alternative: Cloners != Epic, p_value: 0.00000\n",
|
||
"alternative: Cloners < Epic, p_value: 0.00000\n",
|
||
"alternative: Cloners > Epic, p_value: 1.00000\n",
|
||
"Cloners & Epic, delta: -0.19847, small\n",
|
||
"alternative: Cloners != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners > Incorporates, p_value: 1.00000\n",
|
||
"Cloners & Incorporates, delta: -0.20064, small\n",
|
||
"alternative: Cloners != Issue split, p_value: 0.00000\n",
|
||
"alternative: Cloners < Issue split, p_value: 0.00000\n",
|
||
"alternative: Cloners > Issue split, p_value: 1.00000\n",
|
||
"Cloners & Issue split, delta: -0.24757, small\n",
|
||
"alternative: Cloners != Related, p_value: 0.00000\n",
|
||
"alternative: Cloners < Related, p_value: 0.00000\n",
|
||
"alternative: Cloners > Related, p_value: 1.00000\n",
|
||
"Cloners & Related, delta: -0.30681, small\n",
|
||
"alternative: Cloners != Subtask, p_value: 0.00000\n",
|
||
"alternative: Cloners < Subtask, p_value: 1.00000\n",
|
||
"alternative: Cloners > Subtask, p_value: 0.00000\n",
|
||
"Cloners & Subtask, delta: 0.20988, small\n",
|
||
"alternative: Cloners != Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners < Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners > Triggers, p_value: 1.00000\n",
|
||
"Cloners & Triggers, delta: -0.27835, small\n",
|
||
"alternative: Depend != Document, p_value: 0.00000\n",
|
||
"alternative: Depend < Document, p_value: 0.00000\n",
|
||
"alternative: Depend > Document, p_value: 1.00000\n",
|
||
"Depend & Document, delta: -0.25545, small\n",
|
||
"alternative: Depend != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend > Duplicate, p_value: 1.00000\n",
|
||
"Depend & Duplicate, delta: -0.19968, small\n",
|
||
"alternative: Depend != Epic, p_value: 0.00000\n",
|
||
"alternative: Depend < Epic, p_value: 0.00000\n",
|
||
"alternative: Depend > Epic, p_value: 1.00000\n",
|
||
"Depend & Epic, delta: -0.13897, negligible\n",
|
||
"alternative: Depend != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Depend < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Depend > Incorporates, p_value: 1.00000\n",
|
||
"Depend & Incorporates, delta: -0.14232, negligible\n",
|
||
"alternative: Depend != Issue split, p_value: 0.00000\n",
|
||
"alternative: Depend < Issue split, p_value: 0.00000\n",
|
||
"alternative: Depend > Issue split, p_value: 1.00000\n",
|
||
"Depend & Issue split, delta: -0.18536, small\n",
|
||
"alternative: Depend != Related, p_value: 0.00000\n",
|
||
"alternative: Depend < Related, p_value: 0.00000\n",
|
||
"alternative: Depend > Related, p_value: 1.00000\n",
|
||
"Depend & Related, delta: -0.25355, small\n",
|
||
"alternative: Depend != Subtask, p_value: 0.00000\n",
|
||
"alternative: Depend < Subtask, p_value: 1.00000\n",
|
||
"alternative: Depend > Subtask, p_value: 0.00000\n",
|
||
"Depend & Subtask, delta: 0.26188, small\n",
|
||
"alternative: Depend != Triggers, p_value: 0.00000\n",
|
||
"alternative: Depend < Triggers, p_value: 0.00000\n",
|
||
"alternative: Depend > Triggers, p_value: 1.00000\n",
|
||
"Depend & Triggers, delta: -0.22060, small\n",
|
||
"alternative: Document != Duplicate, p_value: 0.00003\n",
|
||
"alternative: Document < Duplicate, p_value: 0.99999\n",
|
||
"alternative: Document > Duplicate, p_value: 0.00001\n",
|
||
"Document & Duplicate, delta: 0.06958, negligible\n",
|
||
"alternative: Document != Epic, p_value: 0.00000\n",
|
||
"alternative: Document < Epic, p_value: 1.00000\n",
|
||
"alternative: Document > Epic, p_value: 0.00000\n",
|
||
"Document & Epic, delta: 0.09844, negligible\n",
|
||
"alternative: Document != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Document < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Document > Incorporates, p_value: 0.00000\n",
|
||
"Document & Incorporates, delta: 0.15063, small\n",
|
||
"alternative: Document != Issue split, p_value: 0.00003\n",
|
||
"alternative: Document < Issue split, p_value: 0.99999\n",
|
||
"alternative: Document > Issue split, p_value: 0.00001\n",
|
||
"Document & Issue split, delta: 0.11258, negligible\n",
|
||
"alternative: Document != Related, p_value: 0.89815\n",
|
||
"alternative: Document < Related, p_value: 0.55093\n",
|
||
"alternative: Document > Related, p_value: 0.44907\n",
|
||
"Document & Related, delta: 0.00195, negligible\n",
|
||
"alternative: Document != Subtask, p_value: 0.00000\n",
|
||
"alternative: Document < Subtask, p_value: 1.00000\n",
|
||
"alternative: Document > Subtask, p_value: 0.00000\n",
|
||
"Document & Subtask, delta: 0.44926, medium\n",
|
||
"alternative: Document != Triggers, p_value: 0.01995\n",
|
||
"alternative: Document < Triggers, p_value: 0.99003\n",
|
||
"alternative: Document > Triggers, p_value: 0.00997\n",
|
||
"Document & Triggers, delta: 0.07277, negligible\n",
|
||
"alternative: Duplicate != Epic, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Epic, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Epic, p_value: 0.00000\n",
|
||
"Duplicate & Epic, delta: 0.04928, negligible\n",
|
||
"alternative: Duplicate != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Incorporates, p_value: 0.00000\n",
|
||
"Duplicate & Incorporates, delta: 0.06757, negligible\n",
|
||
"alternative: Duplicate != Issue split, p_value: 0.82617\n",
|
||
"alternative: Duplicate < Issue split, p_value: 0.58692\n",
|
||
"alternative: Duplicate > Issue split, p_value: 0.41309\n",
|
||
"Duplicate & Issue split, delta: 0.00514, negligible\n",
|
||
"alternative: Duplicate != Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate > Related, p_value: 1.00000\n",
|
||
"Duplicate & Related, delta: -0.06020, negligible\n",
|
||
"alternative: Duplicate != Subtask, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Subtask, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Subtask, p_value: 0.00000\n",
|
||
"Duplicate & Subtask, delta: 0.41048, medium\n",
|
||
"alternative: Duplicate != Triggers, p_value: 0.51429\n",
|
||
"alternative: Duplicate < Triggers, p_value: 0.25714\n",
|
||
"alternative: Duplicate > Triggers, p_value: 0.74286\n",
|
||
"Duplicate & Triggers, delta: -0.01846, negligible\n",
|
||
"alternative: Epic != Incorporates, p_value: 0.00117\n",
|
||
"alternative: Epic < Incorporates, p_value: 0.99942\n",
|
||
"alternative: Epic > Incorporates, p_value: 0.00058\n",
|
||
"Epic & Incorporates, delta: 0.01837, negligible\n",
|
||
"alternative: Epic != Issue split, p_value: 0.58270\n",
|
||
"alternative: Epic < Issue split, p_value: 0.29135\n",
|
||
"alternative: Epic > Issue split, p_value: 0.70865\n",
|
||
"Epic & Issue split, delta: -0.01234, negligible\n",
|
||
"alternative: Epic != Related, p_value: 0.00000\n",
|
||
"alternative: Epic < Related, p_value: 0.00000\n",
|
||
"alternative: Epic > Related, p_value: 1.00000\n",
|
||
"Epic & Related, delta: -0.10693, negligible\n",
|
||
"alternative: Epic != Subtask, p_value: 0.00000\n",
|
||
"alternative: Epic < Subtask, p_value: 1.00000\n",
|
||
"alternative: Epic > Subtask, p_value: 0.00000\n",
|
||
"Epic & Subtask, delta: 0.36429, medium\n",
|
||
"alternative: Epic != Triggers, p_value: 0.05445\n",
|
||
"alternative: Epic < Triggers, p_value: 0.02723\n",
|
||
"alternative: Epic > Triggers, p_value: 0.97277\n",
|
||
"Epic & Triggers, delta: -0.05294, negligible\n",
|
||
"alternative: Incorporates != Issue split, p_value: 0.00277\n",
|
||
"alternative: Incorporates < Issue split, p_value: 0.00138\n",
|
||
"alternative: Incorporates > Issue split, p_value: 0.99862\n",
|
||
"Incorporates & Issue split, delta: -0.06862, negligible\n",
|
||
"alternative: Incorporates != Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates > Related, p_value: 1.00000\n",
|
||
"Incorporates & Related, delta: -0.13070, negligible\n",
|
||
"alternative: Incorporates != Subtask, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Subtask, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Subtask, p_value: 0.00000\n",
|
||
"Incorporates & Subtask, delta: 0.37533, medium\n",
|
||
"alternative: Incorporates != Triggers, p_value: 0.00084\n",
|
||
"alternative: Incorporates < Triggers, p_value: 0.00042\n",
|
||
"alternative: Incorporates > Triggers, p_value: 0.99958\n",
|
||
"Incorporates & Triggers, delta: -0.09318, negligible\n",
|
||
"alternative: Issue split != Related, p_value: 0.00024\n",
|
||
"alternative: Issue split < Related, p_value: 0.00012\n",
|
||
"alternative: Issue split > Related, p_value: 0.99988\n",
|
||
"Issue split & Related, delta: -0.08258, negligible\n",
|
||
"alternative: Issue split != Subtask, p_value: 0.00000\n",
|
||
"alternative: Issue split < Subtask, p_value: 1.00000\n",
|
||
"alternative: Issue split > Subtask, p_value: 0.00000\n",
|
||
"Issue split & Subtask, delta: 0.40961, medium\n",
|
||
"alternative: Issue split != Triggers, p_value: 0.38674\n",
|
||
"alternative: Issue split < Triggers, p_value: 0.19337\n",
|
||
"alternative: Issue split > Triggers, p_value: 0.80668\n",
|
||
"Issue split & Triggers, delta: -0.03064, negligible\n",
|
||
"alternative: Related != Subtask, p_value: 0.00000\n",
|
||
"alternative: Related < Subtask, p_value: 1.00000\n",
|
||
"alternative: Related > Subtask, p_value: 0.00000\n",
|
||
"Related & Subtask, delta: 0.45559, medium\n",
|
||
"alternative: Related != Triggers, p_value: 0.05989\n",
|
||
"alternative: Related < Triggers, p_value: 0.97006\n",
|
||
"alternative: Related > Triggers, p_value: 0.02994\n",
|
||
"Related & Triggers, delta: 0.05188, negligible\n",
|
||
"alternative: Subtask != Triggers, p_value: 0.00000\n",
|
||
"alternative: Subtask < Triggers, p_value: 0.00000\n",
|
||
"alternative: Subtask > Triggers, p_value: 1.00000\n",
|
||
"Subtask & Triggers, delta: -0.43441, medium\n",
|
||
"LTI\n",
|
||
"Kruskal-Wallis H, p_value: 0.00000\n",
|
||
"alternative: Account != Blocks, p_value: 0.00000\n",
|
||
"alternative: Account < Blocks, p_value: 0.00000\n",
|
||
"alternative: Account > Blocks, p_value: 1.00000\n",
|
||
"Account & Blocks, delta: -0.65084, large\n",
|
||
"alternative: Account != Causality, p_value: 0.00000\n",
|
||
"alternative: Account < Causality, p_value: 0.00000\n",
|
||
"alternative: Account > Causality, p_value: 1.00000\n",
|
||
"Account & Causality, delta: -0.56331, large\n",
|
||
"alternative: Account != Cloners, p_value: 0.00000\n",
|
||
"alternative: Account < Cloners, p_value: 1.00000\n",
|
||
"alternative: Account > Cloners, p_value: 0.00000\n",
|
||
"Account & Cloners, delta: 0.47119, medium\n",
|
||
"alternative: Account != Depend, p_value: 0.88045\n",
|
||
"alternative: Account < Depend, p_value: 0.55979\n",
|
||
"alternative: Account > Depend, p_value: 0.44023\n",
|
||
"Account & Depend, delta: 0.00410, negligible\n",
|
||
"alternative: Account != Document, p_value: 0.92588\n",
|
||
"alternative: Account < Document, p_value: 0.53709\n",
|
||
"alternative: Account > Document, p_value: 0.46294\n",
|
||
"Account & Document, delta: 0.00270, negligible\n",
|
||
"alternative: Account != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Account < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Account > Duplicate, p_value: 1.00000\n",
|
||
"Account & Duplicate, delta: -0.46951, medium\n",
|
||
"alternative: Account != Epic, p_value: 0.00000\n",
|
||
"alternative: Account < Epic, p_value: 1.00000\n",
|
||
"alternative: Account > Epic, p_value: 0.00000\n",
|
||
"Account & Epic, delta: 0.47247, medium\n",
|
||
"alternative: Account != Incorporates, p_value: 0.00002\n",
|
||
"alternative: Account < Incorporates, p_value: 0.00001\n",
|
||
"alternative: Account > Incorporates, p_value: 0.99999\n",
|
||
"Account & Incorporates, delta: -0.10793, negligible\n",
|
||
"alternative: Account != Issue split, p_value: 0.35263\n",
|
||
"alternative: Account < Issue split, p_value: 0.17632\n",
|
||
"alternative: Account > Issue split, p_value: 0.82373\n",
|
||
"Account & Issue split, delta: -0.03104, negligible\n",
|
||
"alternative: Account != Related, p_value: 0.00000\n",
|
||
"alternative: Account < Related, p_value: 0.00000\n",
|
||
"alternative: Account > Related, p_value: 1.00000\n",
|
||
"Account & Related, delta: -0.20565, small\n",
|
||
"alternative: Account != Subtask, p_value: 0.00000\n",
|
||
"alternative: Account < Subtask, p_value: 1.00000\n",
|
||
"alternative: Account > Subtask, p_value: 0.00000\n",
|
||
"Account & Subtask, delta: 0.78272, large\n",
|
||
"alternative: Account != Triggers, p_value: 0.00010\n",
|
||
"alternative: Account < Triggers, p_value: 0.99995\n",
|
||
"alternative: Account > Triggers, p_value: 0.00005\n",
|
||
"Account & Triggers, delta: 0.14420, negligible\n",
|
||
"alternative: Blocks != Causality, p_value: 0.00000\n",
|
||
"alternative: Blocks < Causality, p_value: 1.00000\n",
|
||
"alternative: Blocks > Causality, p_value: 0.00000\n",
|
||
"Blocks & Causality, delta: 0.28030, small\n",
|
||
"alternative: Blocks != Cloners, p_value: 0.00000\n",
|
||
"alternative: Blocks < Cloners, p_value: 1.00000\n",
|
||
"alternative: Blocks > Cloners, p_value: 0.00000\n",
|
||
"Blocks & Cloners, delta: 0.81122, large\n",
|
||
"alternative: Blocks != Depend, p_value: 0.00000\n",
|
||
"alternative: Blocks < Depend, p_value: 1.00000\n",
|
||
"alternative: Blocks > Depend, p_value: 0.00000\n",
|
||
"Blocks & Depend, delta: 0.65011, large\n",
|
||
"alternative: Blocks != Document, p_value: 0.00000\n",
|
||
"alternative: Blocks < Document, p_value: 1.00000\n",
|
||
"alternative: Blocks > Document, p_value: 0.00000\n",
|
||
"Blocks & Document, delta: 0.64933, large\n",
|
||
"alternative: Blocks != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Blocks < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Blocks > Duplicate, p_value: 0.00000\n",
|
||
"Blocks & Duplicate, delta: 0.47639, large\n",
|
||
"alternative: Blocks != Epic, p_value: 0.00000\n",
|
||
"alternative: Blocks < Epic, p_value: 1.00000\n",
|
||
"alternative: Blocks > Epic, p_value: 0.00000\n",
|
||
"Blocks & Epic, delta: 0.83364, large\n",
|
||
"alternative: Blocks != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Blocks < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Blocks > Incorporates, p_value: 0.00000\n",
|
||
"Blocks & Incorporates, delta: 0.60783, large\n",
|
||
"alternative: Blocks != Issue split, p_value: 0.00000\n",
|
||
"alternative: Blocks < Issue split, p_value: 1.00000\n",
|
||
"alternative: Blocks > Issue split, p_value: 0.00000\n",
|
||
"Blocks & Issue split, delta: 0.63209, large\n",
|
||
"alternative: Blocks != Related, p_value: 0.00000\n",
|
||
"alternative: Blocks < Related, p_value: 1.00000\n",
|
||
"alternative: Blocks > Related, p_value: 0.00000\n",
|
||
"Blocks & Related, delta: 0.52737, large\n",
|
||
"alternative: Blocks != Subtask, p_value: 0.00000\n",
|
||
"alternative: Blocks < Subtask, p_value: 1.00000\n",
|
||
"alternative: Blocks > Subtask, p_value: 0.00000\n",
|
||
"Blocks & Subtask, delta: 0.94663, large\n",
|
||
"alternative: Blocks != Triggers, p_value: 0.00000\n",
|
||
"alternative: Blocks < Triggers, p_value: 1.00000\n",
|
||
"alternative: Blocks > Triggers, p_value: 0.00000\n",
|
||
"Blocks & Triggers, delta: 0.69915, large\n",
|
||
"alternative: Causality != Cloners, p_value: 0.00000\n",
|
||
"alternative: Causality < Cloners, p_value: 1.00000\n",
|
||
"alternative: Causality > Cloners, p_value: 0.00000\n",
|
||
"Causality & Cloners, delta: 0.79578, large\n",
|
||
"alternative: Causality != Depend, p_value: 0.00000\n",
|
||
"alternative: Causality < Depend, p_value: 1.00000\n",
|
||
"alternative: Causality > Depend, p_value: 0.00000\n",
|
||
"Causality & Depend, delta: 0.56792, large\n",
|
||
"alternative: Causality != Document, p_value: 0.00000\n",
|
||
"alternative: Causality < Document, p_value: 1.00000\n",
|
||
"alternative: Causality > Document, p_value: 0.00000\n",
|
||
"Causality & Document, delta: 0.56923, large\n",
|
||
"alternative: Causality != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Causality < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Causality > Duplicate, p_value: 0.00000\n",
|
||
"Causality & Duplicate, delta: 0.33093, medium\n",
|
||
"alternative: Causality != Epic, p_value: 0.00000\n",
|
||
"alternative: Causality < Epic, p_value: 1.00000\n",
|
||
"alternative: Causality > Epic, p_value: 0.00000\n",
|
||
"Causality & Epic, delta: 0.80570, large\n",
|
||
"alternative: Causality != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Causality < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Causality > Incorporates, p_value: 0.00000\n",
|
||
"Causality & Incorporates, delta: 0.51192, large\n",
|
||
"alternative: Causality != Issue split, p_value: 0.00000\n",
|
||
"alternative: Causality < Issue split, p_value: 1.00000\n",
|
||
"alternative: Causality > Issue split, p_value: 0.00000\n",
|
||
"Causality & Issue split, delta: 0.55019, large\n",
|
||
"alternative: Causality != Related, p_value: 0.00000\n",
|
||
"alternative: Causality < Related, p_value: 1.00000\n",
|
||
"alternative: Causality > Related, p_value: 0.00000\n",
|
||
"Causality & Related, delta: 0.38788, medium\n",
|
||
"alternative: Causality != Subtask, p_value: 0.00000\n",
|
||
"alternative: Causality < Subtask, p_value: 1.00000\n",
|
||
"alternative: Causality > Subtask, p_value: 0.00000\n",
|
||
"Causality & Subtask, delta: 0.94488, large\n",
|
||
"alternative: Causality != Triggers, p_value: 0.00000\n",
|
||
"alternative: Causality < Triggers, p_value: 1.00000\n",
|
||
"alternative: Causality > Triggers, p_value: 0.00000\n",
|
||
"Causality & Triggers, delta: 0.63161, large\n",
|
||
"alternative: Cloners != Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners < Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners > Depend, p_value: 1.00000\n",
|
||
"Cloners & Depend, delta: -0.57665, large\n",
|
||
"alternative: Cloners != Document, p_value: 0.00000\n",
|
||
"alternative: Cloners < Document, p_value: 0.00000\n",
|
||
"alternative: Cloners > Document, p_value: 1.00000\n",
|
||
"Cloners & Document, delta: -0.59760, large\n",
|
||
"alternative: Cloners != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners > Duplicate, p_value: 1.00000\n",
|
||
"Cloners & Duplicate, delta: -0.80578, large\n",
|
||
"alternative: Cloners != Epic, p_value: 0.00000\n",
|
||
"alternative: Cloners < Epic, p_value: 1.00000\n",
|
||
"alternative: Cloners > Epic, p_value: 0.00000\n",
|
||
"Cloners & Epic, delta: 0.18746, small\n",
|
||
"alternative: Cloners != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners > Incorporates, p_value: 1.00000\n",
|
||
"Cloners & Incorporates, delta: -0.72651, large\n",
|
||
"alternative: Cloners != Issue split, p_value: 0.00000\n",
|
||
"alternative: Cloners < Issue split, p_value: 0.00000\n",
|
||
"alternative: Cloners > Issue split, p_value: 1.00000\n",
|
||
"Cloners & Issue split, delta: -0.52249, large\n",
|
||
"alternative: Cloners != Related, p_value: 0.00000\n",
|
||
"alternative: Cloners < Related, p_value: 0.00000\n",
|
||
"alternative: Cloners > Related, p_value: 1.00000\n",
|
||
"Cloners & Related, delta: -0.71707, large\n",
|
||
"alternative: Cloners != Subtask, p_value: 0.00000\n",
|
||
"alternative: Cloners < Subtask, p_value: 1.00000\n",
|
||
"alternative: Cloners > Subtask, p_value: 0.00000\n",
|
||
"Cloners & Subtask, delta: 0.78225, large\n",
|
||
"alternative: Cloners != Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners < Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners > Triggers, p_value: 1.00000\n",
|
||
"Cloners & Triggers, delta: -0.44644, medium\n",
|
||
"alternative: Depend != Document, p_value: 0.79511\n",
|
||
"alternative: Depend < Document, p_value: 0.60245\n",
|
||
"alternative: Depend > Document, p_value: 0.39756\n",
|
||
"Depend & Document, delta: 0.00485, negligible\n",
|
||
"alternative: Depend != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend > Duplicate, p_value: 1.00000\n",
|
||
"Depend & Duplicate, delta: -0.50311, large\n",
|
||
"alternative: Depend != Epic, p_value: 0.00000\n",
|
||
"alternative: Depend < Epic, p_value: 1.00000\n",
|
||
"alternative: Depend > Epic, p_value: 0.00000\n",
|
||
"Depend & Epic, delta: 0.52867, large\n",
|
||
"alternative: Depend != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Depend < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Depend > Incorporates, p_value: 1.00000\n",
|
||
"Depend & Incorporates, delta: -0.11738, negligible\n",
|
||
"alternative: Depend != Issue split, p_value: 0.47896\n",
|
||
"alternative: Depend < Issue split, p_value: 0.76053\n",
|
||
"alternative: Depend > Issue split, p_value: 0.23948\n",
|
||
"Depend & Issue split, delta: 0.01768, negligible\n",
|
||
"alternative: Depend != Related, p_value: 0.00000\n",
|
||
"alternative: Depend < Related, p_value: 0.00000\n",
|
||
"alternative: Depend > Related, p_value: 1.00000\n",
|
||
"Depend & Related, delta: -0.21319, small\n",
|
||
"alternative: Depend != Subtask, p_value: 0.00000\n",
|
||
"alternative: Depend < Subtask, p_value: 1.00000\n",
|
||
"alternative: Depend > Subtask, p_value: 0.00000\n",
|
||
"Depend & Subtask, delta: 0.85392, large\n",
|
||
"alternative: Depend != Triggers, p_value: 0.00000\n",
|
||
"alternative: Depend < Triggers, p_value: 1.00000\n",
|
||
"alternative: Depend > Triggers, p_value: 0.00000\n",
|
||
"Depend & Triggers, delta: 0.15158, small\n",
|
||
"alternative: Document != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Document < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Document > Duplicate, p_value: 1.00000\n",
|
||
"Document & Duplicate, delta: -0.49150, large\n",
|
||
"alternative: Document != Epic, p_value: 0.00000\n",
|
||
"alternative: Document < Epic, p_value: 1.00000\n",
|
||
"alternative: Document > Epic, p_value: 0.00000\n",
|
||
"Document & Epic, delta: 0.53512, large\n",
|
||
"alternative: Document != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Document < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Document > Incorporates, p_value: 1.00000\n",
|
||
"Document & Incorporates, delta: -0.11897, negligible\n",
|
||
"alternative: Document != Issue split, p_value: 0.24824\n",
|
||
"alternative: Document < Issue split, p_value: 0.87589\n",
|
||
"alternative: Document > Issue split, p_value: 0.12412\n",
|
||
"Document & Issue split, delta: 0.03106, negligible\n",
|
||
"alternative: Document != Related, p_value: 0.00000\n",
|
||
"alternative: Document < Related, p_value: 0.00000\n",
|
||
"alternative: Document > Related, p_value: 1.00000\n",
|
||
"Document & Related, delta: -0.21514, small\n",
|
||
"alternative: Document != Subtask, p_value: 0.00000\n",
|
||
"alternative: Document < Subtask, p_value: 1.00000\n",
|
||
"alternative: Document > Subtask, p_value: 0.00000\n",
|
||
"Document & Subtask, delta: 0.85389, large\n",
|
||
"alternative: Document != Triggers, p_value: 0.00000\n",
|
||
"alternative: Document < Triggers, p_value: 1.00000\n",
|
||
"alternative: Document > Triggers, p_value: 0.00000\n",
|
||
"Document & Triggers, delta: 0.14848, small\n",
|
||
"alternative: Duplicate != Epic, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Epic, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Epic, p_value: 0.00000\n",
|
||
"Duplicate & Epic, delta: 0.78676, large\n",
|
||
"alternative: Duplicate != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Incorporates, p_value: 0.00000\n",
|
||
"Duplicate & Incorporates, delta: 0.41183, medium\n",
|
||
"alternative: Duplicate != Issue split, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Issue split, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Issue split, p_value: 0.00000\n",
|
||
"Duplicate & Issue split, delta: 0.30689, small\n",
|
||
"alternative: Duplicate != Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Related, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Related, p_value: 0.00000\n",
|
||
"Duplicate & Related, delta: 0.23037, small\n",
|
||
"alternative: Duplicate != Subtask, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Subtask, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Subtask, p_value: 0.00000\n",
|
||
"Duplicate & Subtask, delta: 0.96500, large\n",
|
||
"alternative: Duplicate != Triggers, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Triggers, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Triggers, p_value: 0.00000\n",
|
||
"Duplicate & Triggers, delta: 0.61803, large\n",
|
||
"alternative: Epic != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Epic < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Epic > Incorporates, p_value: 1.00000\n",
|
||
"Epic & Incorporates, delta: -0.63608, large\n",
|
||
"alternative: Epic != Issue split, p_value: 0.00000\n",
|
||
"alternative: Epic < Issue split, p_value: 0.00000\n",
|
||
"alternative: Epic > Issue split, p_value: 1.00000\n",
|
||
"Epic & Issue split, delta: -0.53666, large\n",
|
||
"alternative: Epic != Related, p_value: 0.00000\n",
|
||
"alternative: Epic < Related, p_value: 0.00000\n",
|
||
"alternative: Epic > Related, p_value: 1.00000\n",
|
||
"Epic & Related, delta: -0.66040, large\n",
|
||
"alternative: Epic != Subtask, p_value: 0.00000\n",
|
||
"alternative: Epic < Subtask, p_value: 1.00000\n",
|
||
"alternative: Epic > Subtask, p_value: 0.00000\n",
|
||
"Epic & Subtask, delta: 0.43735, medium\n",
|
||
"alternative: Epic != Triggers, p_value: 0.00000\n",
|
||
"alternative: Epic < Triggers, p_value: 0.00000\n",
|
||
"alternative: Epic > Triggers, p_value: 1.00000\n",
|
||
"Epic & Triggers, delta: -0.41599, medium\n",
|
||
"alternative: Incorporates != Issue split, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Issue split, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Issue split, p_value: 0.00000\n",
|
||
"Incorporates & Issue split, delta: 0.13131, negligible\n",
|
||
"alternative: Incorporates != Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates > Related, p_value: 1.00000\n",
|
||
"Incorporates & Related, delta: -0.11174, negligible\n",
|
||
"alternative: Incorporates != Subtask, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Subtask, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Subtask, p_value: 0.00000\n",
|
||
"Incorporates & Subtask, delta: 0.93050, large\n",
|
||
"alternative: Incorporates != Triggers, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Triggers, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Triggers, p_value: 0.00000\n",
|
||
"Incorporates & Triggers, delta: 0.27487, small\n",
|
||
"alternative: Issue split != Related, p_value: 0.00000\n",
|
||
"alternative: Issue split < Related, p_value: 0.00000\n",
|
||
"alternative: Issue split > Related, p_value: 1.00000\n",
|
||
"Issue split & Related, delta: -0.22042, small\n",
|
||
"alternative: Issue split != Subtask, p_value: 0.00000\n",
|
||
"alternative: Issue split < Subtask, p_value: 1.00000\n",
|
||
"alternative: Issue split > Subtask, p_value: 0.00000\n",
|
||
"Issue split & Subtask, delta: 0.89734, large\n",
|
||
"alternative: Issue split != Triggers, p_value: 0.00952\n",
|
||
"alternative: Issue split < Triggers, p_value: 0.99525\n",
|
||
"alternative: Issue split > Triggers, p_value: 0.00476\n",
|
||
"Issue split & Triggers, delta: 0.09167, negligible\n",
|
||
"alternative: Related != Subtask, p_value: 0.00000\n",
|
||
"alternative: Related < Subtask, p_value: 1.00000\n",
|
||
"alternative: Related > Subtask, p_value: 0.00000\n",
|
||
"Related & Subtask, delta: 0.91769, large\n",
|
||
"alternative: Related != Triggers, p_value: 0.00000\n",
|
||
"alternative: Related < Triggers, p_value: 1.00000\n",
|
||
"alternative: Related > Triggers, p_value: 0.00000\n",
|
||
"Related & Triggers, delta: 0.34337, medium\n",
|
||
"alternative: Subtask != Triggers, p_value: 0.00000\n",
|
||
"alternative: Subtask < Triggers, p_value: 0.00000\n",
|
||
"alternative: Subtask > Triggers, p_value: 1.00000\n",
|
||
"Subtask & Triggers, delta: -0.77271, large\n",
|
||
"------------------------------\n",
|
||
"NUM_COMMENTS\n",
|
||
"Kruskal-Wallis H, p_value: 0.00000\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Blocks, p_value: 0.98414\n",
|
||
"alternative: Account < Blocks, p_value: 0.49207\n",
|
||
"alternative: Account > Blocks, p_value: 0.50793\n",
|
||
"Account & Blocks, delta: -0.00039, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Causality, p_value: 0.72447\n",
|
||
"alternative: Account < Causality, p_value: 0.63777\n",
|
||
"alternative: Account > Causality, p_value: 0.36224\n",
|
||
"Account & Causality, delta: 0.00716, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Cloners, p_value: 0.00000\n",
|
||
"alternative: Account < Cloners, p_value: 1.00000\n",
|
||
"alternative: Account > Cloners, p_value: 0.00000\n",
|
||
"Account & Cloners, delta: 0.14133, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Depend, p_value: 0.00002\n",
|
||
"alternative: Account < Depend, p_value: 0.99999\n",
|
||
"alternative: Account > Depend, p_value: 0.00001\n",
|
||
"Account & Depend, delta: 0.09016, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Document, p_value: 0.85629\n",
|
||
"alternative: Account < Document, p_value: 0.57187\n",
|
||
"alternative: Account > Document, p_value: 0.42815\n",
|
||
"Account & Document, delta: 0.00397, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Duplicate, p_value: 0.54114\n",
|
||
"alternative: Account < Duplicate, p_value: 0.72943\n",
|
||
"alternative: Account > Duplicate, p_value: 0.27057\n",
|
||
"Account & Duplicate, delta: 0.01207, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Epic, p_value: 0.00000\n",
|
||
"alternative: Account < Epic, p_value: 1.00000\n",
|
||
"alternative: Account > Epic, p_value: 0.00000\n",
|
||
"Account & Epic, delta: 0.29513, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Incorporates, p_value: 0.00015\n",
|
||
"alternative: Account < Incorporates, p_value: 0.99993\n",
|
||
"alternative: Account > Incorporates, p_value: 0.00007\n",
|
||
"Account & Incorporates, delta: 0.07422, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Issue split, p_value: 0.00000\n",
|
||
"alternative: Account < Issue split, p_value: 1.00000\n",
|
||
"alternative: Account > Issue split, p_value: 0.00000\n",
|
||
"Account & Issue split, delta: 0.19279, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Related, p_value: 0.01250\n",
|
||
"alternative: Account < Related, p_value: 0.00625\n",
|
||
"alternative: Account > Related, p_value: 0.99375\n",
|
||
"Account & Related, delta: -0.04823, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Subtask, p_value: 0.00000\n",
|
||
"alternative: Account < Subtask, p_value: 1.00000\n",
|
||
"alternative: Account > Subtask, p_value: 0.00000\n",
|
||
"Account & Subtask, delta: 0.32325, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Account != Triggers, p_value: 0.12561\n",
|
||
"alternative: Account < Triggers, p_value: 0.93721\n",
|
||
"alternative: Account > Triggers, p_value: 0.06281\n",
|
||
"Account & Triggers, delta: 0.04274, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Causality, p_value: 0.32632\n",
|
||
"alternative: Blocks < Causality, p_value: 0.83684\n",
|
||
"alternative: Blocks > Causality, p_value: 0.16316\n",
|
||
"Blocks & Causality, delta: 0.00745, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Cloners, p_value: 0.00000\n",
|
||
"alternative: Blocks < Cloners, p_value: 1.00000\n",
|
||
"alternative: Blocks > Cloners, p_value: 0.00000\n",
|
||
"Blocks & Cloners, delta: 0.14965, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Depend, p_value: 0.00000\n",
|
||
"alternative: Blocks < Depend, p_value: 1.00000\n",
|
||
"alternative: Blocks > Depend, p_value: 0.00000\n",
|
||
"Blocks & Depend, delta: 0.09469, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Document, p_value: 0.74244\n",
|
||
"alternative: Blocks < Document, p_value: 0.62878\n",
|
||
"alternative: Blocks > Document, p_value: 0.37122\n",
|
||
"Blocks & Document, delta: 0.00370, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Duplicate, p_value: 0.02577\n",
|
||
"alternative: Blocks < Duplicate, p_value: 0.98711\n",
|
||
"alternative: Blocks > Duplicate, p_value: 0.01289\n",
|
||
"Blocks & Duplicate, delta: 0.01330, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Epic, p_value: 0.00000\n",
|
||
"alternative: Blocks < Epic, p_value: 1.00000\n",
|
||
"alternative: Blocks > Epic, p_value: 0.00000\n",
|
||
"Blocks & Epic, delta: 0.31015, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Blocks < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Blocks > Incorporates, p_value: 0.00000\n",
|
||
"Blocks & Incorporates, delta: 0.07893, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Issue split, p_value: 0.00000\n",
|
||
"alternative: Blocks < Issue split, p_value: 1.00000\n",
|
||
"alternative: Blocks > Issue split, p_value: 0.00000\n",
|
||
"Blocks & Issue split, delta: 0.20259, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Related, p_value: 0.00000\n",
|
||
"alternative: Blocks < Related, p_value: 0.00000\n",
|
||
"alternative: Blocks > Related, p_value: 1.00000\n",
|
||
"Blocks & Related, delta: -0.05049, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Subtask, p_value: 0.00000\n",
|
||
"alternative: Blocks < Subtask, p_value: 1.00000\n",
|
||
"alternative: Blocks > Subtask, p_value: 0.00000\n",
|
||
"Blocks & Subtask, delta: 0.33999, medium\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Blocks != Triggers, p_value: 0.02929\n",
|
||
"alternative: Blocks < Triggers, p_value: 0.98536\n",
|
||
"alternative: Blocks > Triggers, p_value: 0.01464\n",
|
||
"Blocks & Triggers, delta: 0.04503, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Cloners, p_value: 0.00000\n",
|
||
"alternative: Causality < Cloners, p_value: 1.00000\n",
|
||
"alternative: Causality > Cloners, p_value: 0.00000\n",
|
||
"Causality & Cloners, delta: 0.13961, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Depend, p_value: 0.00000\n",
|
||
"alternative: Causality < Depend, p_value: 1.00000\n",
|
||
"alternative: Causality > Depend, p_value: 0.00000\n",
|
||
"Causality & Depend, delta: 0.08611, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Document, p_value: 0.77312\n",
|
||
"alternative: Causality < Document, p_value: 0.38656\n",
|
||
"alternative: Causality > Document, p_value: 0.61344\n",
|
||
"Causality & Document, delta: -0.00368, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Duplicate, p_value: 0.51332\n",
|
||
"alternative: Causality < Duplicate, p_value: 0.74334\n",
|
||
"alternative: Causality > Duplicate, p_value: 0.25666\n",
|
||
"Causality & Duplicate, delta: 0.00553, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Epic, p_value: 0.00000\n",
|
||
"alternative: Causality < Epic, p_value: 1.00000\n",
|
||
"alternative: Causality > Epic, p_value: 0.00000\n",
|
||
"Causality & Epic, delta: 0.29801, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Causality < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Causality > Incorporates, p_value: 0.00000\n",
|
||
"Causality & Incorporates, delta: 0.07020, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Issue split, p_value: 0.00000\n",
|
||
"alternative: Causality < Issue split, p_value: 1.00000\n",
|
||
"alternative: Causality > Issue split, p_value: 0.00000\n",
|
||
"Causality & Issue split, delta: 0.19243, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Related, p_value: 0.00000\n",
|
||
"alternative: Causality < Related, p_value: 0.00000\n",
|
||
"alternative: Causality > Related, p_value: 1.00000\n",
|
||
"Causality & Related, delta: -0.05717, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Subtask, p_value: 0.00000\n",
|
||
"alternative: Causality < Subtask, p_value: 1.00000\n",
|
||
"alternative: Causality > Subtask, p_value: 0.00000\n",
|
||
"Causality & Subtask, delta: 0.32723, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Causality != Triggers, p_value: 0.08621\n",
|
||
"alternative: Causality < Triggers, p_value: 0.95690\n",
|
||
"alternative: Causality > Triggers, p_value: 0.04311\n",
|
||
"Causality & Triggers, delta: 0.03688, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners < Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners > Depend, p_value: 1.00000\n",
|
||
"Cloners & Depend, delta: -0.04968, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Document, p_value: 0.00000\n",
|
||
"alternative: Cloners < Document, p_value: 0.00000\n",
|
||
"alternative: Cloners > Document, p_value: 1.00000\n",
|
||
"Cloners & Document, delta: -0.13878, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners > Duplicate, p_value: 1.00000\n",
|
||
"Cloners & Duplicate, delta: -0.13785, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Epic, p_value: 0.00000\n",
|
||
"alternative: Cloners < Epic, p_value: 1.00000\n",
|
||
"alternative: Cloners > Epic, p_value: 0.00000\n",
|
||
"Cloners & Epic, delta: 0.17967, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners > Incorporates, p_value: 1.00000\n",
|
||
"Cloners & Incorporates, delta: -0.07882, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Issue split, p_value: 0.00010\n",
|
||
"alternative: Cloners < Issue split, p_value: 0.99995\n",
|
||
"alternative: Cloners > Issue split, p_value: 0.00005\n",
|
||
"Cloners & Issue split, delta: 0.06767, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Related, p_value: 0.00000\n",
|
||
"alternative: Cloners < Related, p_value: 0.00000\n",
|
||
"alternative: Cloners > Related, p_value: 1.00000\n",
|
||
"Cloners & Related, delta: -0.20125, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Subtask, p_value: 0.00000\n",
|
||
"alternative: Cloners < Subtask, p_value: 1.00000\n",
|
||
"alternative: Cloners > Subtask, p_value: 0.00000\n",
|
||
"Cloners & Subtask, delta: 0.21273, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Cloners != Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners < Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners > Triggers, p_value: 1.00000\n",
|
||
"Cloners & Triggers, delta: -0.09822, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Document, p_value: 0.00000\n",
|
||
"alternative: Depend < Document, p_value: 0.00000\n",
|
||
"alternative: Depend > Document, p_value: 1.00000\n",
|
||
"Depend & Document, delta: -0.08713, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend > Duplicate, p_value: 1.00000\n",
|
||
"Depend & Duplicate, delta: -0.08281, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Epic, p_value: 0.00000\n",
|
||
"alternative: Depend < Epic, p_value: 1.00000\n",
|
||
"alternative: Depend > Epic, p_value: 0.00000\n",
|
||
"Depend & Epic, delta: 0.21552, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Incorporates, p_value: 0.01991\n",
|
||
"alternative: Depend < Incorporates, p_value: 0.00996\n",
|
||
"alternative: Depend > Incorporates, p_value: 0.99004\n",
|
||
"Depend & Incorporates, delta: -0.02268, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Issue split, p_value: 0.00000\n",
|
||
"alternative: Depend < Issue split, p_value: 1.00000\n",
|
||
"alternative: Depend > Issue split, p_value: 0.00000\n",
|
||
"Depend & Issue split, delta: 0.10947, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Related, p_value: 0.00000\n",
|
||
"alternative: Depend < Related, p_value: 0.00000\n",
|
||
"alternative: Depend > Related, p_value: 1.00000\n",
|
||
"Depend & Related, delta: -0.14413, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Subtask, p_value: 0.00000\n",
|
||
"alternative: Depend < Subtask, p_value: 1.00000\n",
|
||
"alternative: Depend > Subtask, p_value: 0.00000\n",
|
||
"Depend & Subtask, delta: 0.24590, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Depend != Triggers, p_value: 0.03007\n",
|
||
"alternative: Depend < Triggers, p_value: 0.01504\n",
|
||
"alternative: Depend > Triggers, p_value: 0.98496\n",
|
||
"Depend & Triggers, delta: -0.04780, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Duplicate, p_value: 0.45266\n",
|
||
"alternative: Document < Duplicate, p_value: 0.77367\n",
|
||
"alternative: Document > Duplicate, p_value: 0.22633\n",
|
||
"Document & Duplicate, delta: 0.00890, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Epic, p_value: 0.00000\n",
|
||
"alternative: Document < Epic, p_value: 1.00000\n",
|
||
"alternative: Document > Epic, p_value: 0.00000\n",
|
||
"Document & Epic, delta: 0.29202, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Document < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Document > Incorporates, p_value: 0.00000\n",
|
||
"Document & Incorporates, delta: 0.07211, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Issue split, p_value: 0.00000\n",
|
||
"alternative: Document < Issue split, p_value: 1.00000\n",
|
||
"alternative: Document > Issue split, p_value: 0.00000\n",
|
||
"Document & Issue split, delta: 0.18965, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Related, p_value: 0.00000\n",
|
||
"alternative: Document < Related, p_value: 0.00000\n",
|
||
"alternative: Document > Related, p_value: 1.00000\n",
|
||
"Document & Related, delta: -0.05178, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Subtask, p_value: 0.00000\n",
|
||
"alternative: Document < Subtask, p_value: 1.00000\n",
|
||
"alternative: Document > Subtask, p_value: 0.00000\n",
|
||
"Document & Subtask, delta: 0.31993, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Document != Triggers, p_value: 0.08852\n",
|
||
"alternative: Document < Triggers, p_value: 0.95574\n",
|
||
"alternative: Document > Triggers, p_value: 0.04426\n",
|
||
"Document & Triggers, delta: 0.03923, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Duplicate != Epic, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Epic, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Epic, p_value: 0.00000\n",
|
||
"Duplicate & Epic, delta: 0.30179, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Duplicate != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Incorporates, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Incorporates, p_value: 0.00000\n",
|
||
"Duplicate & Incorporates, delta: 0.06555, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Duplicate != Issue split, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Issue split, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Issue split, p_value: 0.00000\n",
|
||
"Duplicate & Issue split, delta: 0.19273, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Duplicate != Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate > Related, p_value: 1.00000\n",
|
||
"Duplicate & Related, delta: -0.06441, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Duplicate != Subtask, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Subtask, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Subtask, p_value: 0.00000\n",
|
||
"Duplicate & Subtask, delta: 0.33230, medium\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Duplicate != Triggers, p_value: 0.11780\n",
|
||
"alternative: Duplicate < Triggers, p_value: 0.94110\n",
|
||
"alternative: Duplicate > Triggers, p_value: 0.05890\n",
|
||
"Duplicate & Triggers, delta: 0.03280, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Epic != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Epic < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Epic > Incorporates, p_value: 1.00000\n",
|
||
"Epic & Incorporates, delta: -0.25617, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Epic != Issue split, p_value: 0.00000\n",
|
||
"alternative: Epic < Issue split, p_value: 0.00000\n",
|
||
"alternative: Epic > Issue split, p_value: 1.00000\n",
|
||
"Epic & Issue split, delta: -0.10257, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Epic != Related, p_value: 0.00000\n",
|
||
"alternative: Epic < Related, p_value: 0.00000\n",
|
||
"alternative: Epic > Related, p_value: 1.00000\n",
|
||
"Epic & Related, delta: -0.35647, medium\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Epic != Subtask, p_value: 0.00000\n",
|
||
"alternative: Epic < Subtask, p_value: 1.00000\n",
|
||
"alternative: Epic > Subtask, p_value: 0.00000\n",
|
||
"Epic & Subtask, delta: 0.03139, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Epic != Triggers, p_value: 0.00000\n",
|
||
"alternative: Epic < Triggers, p_value: 0.00000\n",
|
||
"alternative: Epic > Triggers, p_value: 1.00000\n",
|
||
"Epic & Triggers, delta: -0.25786, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Incorporates != Issue split, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Issue split, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Issue split, p_value: 0.00000\n",
|
||
"Incorporates & Issue split, delta: 0.14076, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Incorporates != Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates > Related, p_value: 1.00000\n",
|
||
"Incorporates & Related, delta: -0.13271, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Incorporates != Subtask, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Subtask, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Subtask, p_value: 0.00000\n",
|
||
"Incorporates & Subtask, delta: 0.28959, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Incorporates != Triggers, p_value: 0.16804\n",
|
||
"alternative: Incorporates < Triggers, p_value: 0.08402\n",
|
||
"alternative: Incorporates > Triggers, p_value: 0.91598\n",
|
||
"Incorporates & Triggers, delta: -0.02862, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Issue split != Related, p_value: 0.00000\n",
|
||
"alternative: Issue split < Related, p_value: 0.00000\n",
|
||
"alternative: Issue split > Related, p_value: 1.00000\n",
|
||
"Issue split & Related, delta: -0.24961, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Issue split != Subtask, p_value: 0.00000\n",
|
||
"alternative: Issue split < Subtask, p_value: 1.00000\n",
|
||
"alternative: Issue split > Subtask, p_value: 0.00000\n",
|
||
"Issue split & Subtask, delta: 0.13283, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Issue split != Triggers, p_value: 0.00000\n",
|
||
"alternative: Issue split < Triggers, p_value: 0.00000\n",
|
||
"alternative: Issue split > Triggers, p_value: 1.00000\n",
|
||
"Issue split & Triggers, delta: -0.15359, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Related != Subtask, p_value: 0.00000\n",
|
||
"alternative: Related < Subtask, p_value: 1.00000\n",
|
||
"alternative: Related > Subtask, p_value: 0.00000\n",
|
||
"Related & Subtask, delta: 0.38546, medium\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Related != Triggers, p_value: 0.00001\n",
|
||
"alternative: Related < Triggers, p_value: 1.00000\n",
|
||
"alternative: Related > Triggers, p_value: 0.00000\n",
|
||
"Related & Triggers, delta: 0.09371, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"alternative: Subtask != Triggers, p_value: 0.00000\n",
|
||
"alternative: Subtask < Triggers, p_value: 0.00000\n",
|
||
"alternative: Subtask > Triggers, p_value: 1.00000\n",
|
||
"Subtask & Triggers, delta: -0.28701, small\n",
|
||
"------------------------------\n",
|
||
"Solved Proportion:\n",
|
||
"X^2: 4314.90105 p: 0.00000\n",
|
||
"------------------------------\n",
|
||
"STI\n",
|
||
"Kruskal-Wallis H, p_value: 0.00000\n",
|
||
"alternative: Account != Blocks, p_value: 0.00000\n",
|
||
"alternative: Account < Blocks, p_value: 0.00000\n",
|
||
"alternative: Account > Blocks, p_value: 1.00000\n",
|
||
"Account & Blocks, delta: -0.34934, medium\n",
|
||
"alternative: Account != Causality, p_value: 0.00000\n",
|
||
"alternative: Account < Causality, p_value: 0.00000\n",
|
||
"alternative: Account > Causality, p_value: 1.00000\n",
|
||
"Account & Causality, delta: -0.32659, small\n",
|
||
"alternative: Account != Cloners, p_value: 0.00000\n",
|
||
"alternative: Account < Cloners, p_value: 0.00000\n",
|
||
"alternative: Account > Cloners, p_value: 1.00000\n",
|
||
"Account & Cloners, delta: -0.22703, small\n",
|
||
"alternative: Account != Depend, p_value: 0.65955\n",
|
||
"alternative: Account < Depend, p_value: 0.32978\n",
|
||
"alternative: Account > Depend, p_value: 0.67024\n",
|
||
"Account & Depend, delta: -0.01123, negligible\n",
|
||
"alternative: Account != Document, p_value: 0.00000\n",
|
||
"alternative: Account < Document, p_value: 0.00000\n",
|
||
"alternative: Account > Document, p_value: 1.00000\n",
|
||
"Account & Document, delta: -0.29304, small\n",
|
||
"alternative: Account != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Account < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Account > Duplicate, p_value: 1.00000\n",
|
||
"Account & Duplicate, delta: -0.14988, small\n",
|
||
"alternative: Account != Epic, p_value: 0.00000\n",
|
||
"alternative: Account < Epic, p_value: 0.00000\n",
|
||
"alternative: Account > Epic, p_value: 1.00000\n",
|
||
"Account & Epic, delta: -0.17265, small\n",
|
||
"alternative: Account != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Account < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Account > Incorporates, p_value: 1.00000\n",
|
||
"Account & Incorporates, delta: -0.45129, medium\n",
|
||
"alternative: Account != Issue split, p_value: 0.54381\n",
|
||
"alternative: Account < Issue split, p_value: 0.72815\n",
|
||
"alternative: Account > Issue split, p_value: 0.27190\n",
|
||
"Account & Issue split, delta: 0.01989, negligible\n",
|
||
"alternative: Account != Related, p_value: 0.00000\n",
|
||
"alternative: Account < Related, p_value: 0.00000\n",
|
||
"alternative: Account > Related, p_value: 1.00000\n",
|
||
"Account & Related, delta: -0.34287, medium\n",
|
||
"alternative: Account != Subtask, p_value: 0.00005\n",
|
||
"alternative: Account < Subtask, p_value: 0.00002\n",
|
||
"alternative: Account > Subtask, p_value: 0.99998\n",
|
||
"Account & Subtask, delta: -0.09379, negligible\n",
|
||
"alternative: Account != Triggers, p_value: 0.21682\n",
|
||
"alternative: Account < Triggers, p_value: 0.10841\n",
|
||
"alternative: Account > Triggers, p_value: 0.89163\n",
|
||
"Account & Triggers, delta: -0.04270, negligible\n",
|
||
"alternative: Blocks != Causality, p_value: 0.20784\n",
|
||
"alternative: Blocks < Causality, p_value: 0.89608\n",
|
||
"alternative: Blocks > Causality, p_value: 0.10392\n",
|
||
"Blocks & Causality, delta: 0.01212, negligible\n",
|
||
"alternative: Blocks != Cloners, p_value: 0.00000\n",
|
||
"alternative: Blocks < Cloners, p_value: 1.00000\n",
|
||
"alternative: Blocks > Cloners, p_value: 0.00000\n",
|
||
"Blocks & Cloners, delta: 0.11673, negligible\n",
|
||
"alternative: Blocks != Depend, p_value: 0.00000\n",
|
||
"alternative: Blocks < Depend, p_value: 1.00000\n",
|
||
"alternative: Blocks > Depend, p_value: 0.00000\n",
|
||
"Blocks & Depend, delta: 0.35480, medium\n",
|
||
"alternative: Blocks != Document, p_value: 0.00000\n",
|
||
"alternative: Blocks < Document, p_value: 1.00000\n",
|
||
"alternative: Blocks > Document, p_value: 0.00000\n",
|
||
"Blocks & Document, delta: 0.10278, negligible\n",
|
||
"alternative: Blocks != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Blocks < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Blocks > Duplicate, p_value: 0.00000\n",
|
||
"Blocks & Duplicate, delta: 0.14946, small\n",
|
||
"alternative: Blocks != Epic, p_value: 0.00000\n",
|
||
"alternative: Blocks < Epic, p_value: 1.00000\n",
|
||
"alternative: Blocks > Epic, p_value: 0.00000\n",
|
||
"Blocks & Epic, delta: 0.18979, small\n",
|
||
"alternative: Blocks != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Blocks < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Blocks > Incorporates, p_value: 1.00000\n",
|
||
"Blocks & Incorporates, delta: -0.07955, negligible\n",
|
||
"alternative: Blocks != Issue split, p_value: 0.00000\n",
|
||
"alternative: Blocks < Issue split, p_value: 1.00000\n",
|
||
"alternative: Blocks > Issue split, p_value: 0.00000\n",
|
||
"Blocks & Issue split, delta: 0.37348, medium\n",
|
||
"alternative: Blocks != Related, p_value: 0.58446\n",
|
||
"alternative: Blocks < Related, p_value: 0.29223\n",
|
||
"alternative: Blocks > Related, p_value: 0.70777\n",
|
||
"Blocks & Related, delta: -0.00267, negligible\n",
|
||
"alternative: Blocks != Subtask, p_value: 0.00000\n",
|
||
"alternative: Blocks < Subtask, p_value: 1.00000\n",
|
||
"alternative: Blocks > Subtask, p_value: 0.00000\n",
|
||
"Blocks & Subtask, delta: 0.22836, small\n",
|
||
"alternative: Blocks != Triggers, p_value: 0.00000\n",
|
||
"alternative: Blocks < Triggers, p_value: 1.00000\n",
|
||
"alternative: Blocks > Triggers, p_value: 0.00000\n",
|
||
"Blocks & Triggers, delta: 0.31149, small\n",
|
||
"alternative: Causality != Cloners, p_value: 0.00000\n",
|
||
"alternative: Causality < Cloners, p_value: 1.00000\n",
|
||
"alternative: Causality > Cloners, p_value: 0.00000\n",
|
||
"Causality & Cloners, delta: 0.10214, negligible\n",
|
||
"alternative: Causality != Depend, p_value: 0.00000\n",
|
||
"alternative: Causality < Depend, p_value: 1.00000\n",
|
||
"alternative: Causality > Depend, p_value: 0.00000\n",
|
||
"Causality & Depend, delta: 0.32794, small\n",
|
||
"alternative: Causality != Document, p_value: 0.00000\n",
|
||
"alternative: Causality < Document, p_value: 1.00000\n",
|
||
"alternative: Causality > Document, p_value: 0.00000\n",
|
||
"Causality & Document, delta: 0.08915, negligible\n",
|
||
"alternative: Causality != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Causality < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Causality > Duplicate, p_value: 0.00000\n",
|
||
"Causality & Duplicate, delta: 0.13666, negligible\n",
|
||
"alternative: Causality != Epic, p_value: 0.00000\n",
|
||
"alternative: Causality < Epic, p_value: 1.00000\n",
|
||
"alternative: Causality > Epic, p_value: 0.00000\n",
|
||
"Causality & Epic, delta: 0.17375, small\n",
|
||
"alternative: Causality != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Causality < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Causality > Incorporates, p_value: 1.00000\n",
|
||
"Causality & Incorporates, delta: -0.08853, negligible\n",
|
||
"alternative: Causality != Issue split, p_value: 0.00000\n",
|
||
"alternative: Causality < Issue split, p_value: 1.00000\n",
|
||
"alternative: Causality > Issue split, p_value: 0.00000\n",
|
||
"Causality & Issue split, delta: 0.34645, medium\n",
|
||
"alternative: Causality != Related, p_value: 0.09519\n",
|
||
"alternative: Causality < Related, p_value: 0.04759\n",
|
||
"alternative: Causality > Related, p_value: 0.95241\n",
|
||
"Causality & Related, delta: -0.01531, negligible\n",
|
||
"alternative: Causality != Subtask, p_value: 0.00000\n",
|
||
"alternative: Causality < Subtask, p_value: 1.00000\n",
|
||
"alternative: Causality > Subtask, p_value: 0.00000\n",
|
||
"Causality & Subtask, delta: 0.21141, small\n",
|
||
"alternative: Causality != Triggers, p_value: 0.00000\n",
|
||
"alternative: Causality < Triggers, p_value: 1.00000\n",
|
||
"alternative: Causality > Triggers, p_value: 0.00000\n",
|
||
"Causality & Triggers, delta: 0.28900, small\n",
|
||
"alternative: Cloners != Depend, p_value: 0.00000\n",
|
||
"alternative: Cloners < Depend, p_value: 1.00000\n",
|
||
"alternative: Cloners > Depend, p_value: 0.00000\n",
|
||
"Cloners & Depend, delta: 0.22826, small\n",
|
||
"alternative: Cloners != Document, p_value: 0.03926\n",
|
||
"alternative: Cloners < Document, p_value: 0.01963\n",
|
||
"alternative: Cloners > Document, p_value: 0.98037\n",
|
||
"Cloners & Document, delta: -0.02671, negligible\n",
|
||
"alternative: Cloners != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Cloners < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Cloners > Duplicate, p_value: 0.00000\n",
|
||
"Cloners & Duplicate, delta: 0.04436, negligible\n",
|
||
"alternative: Cloners != Epic, p_value: 0.00000\n",
|
||
"alternative: Cloners < Epic, p_value: 1.00000\n",
|
||
"alternative: Cloners > Epic, p_value: 0.00000\n",
|
||
"Cloners & Epic, delta: 0.06666, negligible\n",
|
||
"alternative: Cloners != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Cloners > Incorporates, p_value: 1.00000\n",
|
||
"Cloners & Incorporates, delta: -0.20040, small\n",
|
||
"alternative: Cloners != Issue split, p_value: 0.00000\n",
|
||
"alternative: Cloners < Issue split, p_value: 1.00000\n",
|
||
"alternative: Cloners > Issue split, p_value: 0.00000\n",
|
||
"Cloners & Issue split, delta: 0.24741, small\n",
|
||
"alternative: Cloners != Related, p_value: 0.00000\n",
|
||
"alternative: Cloners < Related, p_value: 0.00000\n",
|
||
"alternative: Cloners > Related, p_value: 1.00000\n",
|
||
"Cloners & Related, delta: -0.11722, negligible\n",
|
||
"alternative: Cloners != Subtask, p_value: 0.00000\n",
|
||
"alternative: Cloners < Subtask, p_value: 1.00000\n",
|
||
"alternative: Cloners > Subtask, p_value: 0.00000\n",
|
||
"Cloners & Subtask, delta: 0.11546, negligible\n",
|
||
"alternative: Cloners != Triggers, p_value: 0.00000\n",
|
||
"alternative: Cloners < Triggers, p_value: 1.00000\n",
|
||
"alternative: Cloners > Triggers, p_value: 0.00000\n",
|
||
"Cloners & Triggers, delta: 0.18828, small\n",
|
||
"alternative: Depend != Document, p_value: 0.00000\n",
|
||
"alternative: Depend < Document, p_value: 0.00000\n",
|
||
"alternative: Depend > Document, p_value: 1.00000\n",
|
||
"Depend & Document, delta: -0.30362, small\n",
|
||
"alternative: Depend != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend < Duplicate, p_value: 0.00000\n",
|
||
"alternative: Depend > Duplicate, p_value: 1.00000\n",
|
||
"Depend & Duplicate, delta: -0.14877, small\n",
|
||
"alternative: Depend != Epic, p_value: 0.00000\n",
|
||
"alternative: Depend < Epic, p_value: 0.00000\n",
|
||
"alternative: Depend > Epic, p_value: 1.00000\n",
|
||
"Depend & Epic, delta: -0.16924, small\n",
|
||
"alternative: Depend != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Depend < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Depend > Incorporates, p_value: 1.00000\n",
|
||
"Depend & Incorporates, delta: -0.46584, medium\n",
|
||
"alternative: Depend != Issue split, p_value: 0.10752\n",
|
||
"alternative: Depend < Issue split, p_value: 0.94625\n",
|
||
"alternative: Depend > Issue split, p_value: 0.05376\n",
|
||
"Depend & Issue split, delta: 0.04198, negligible\n",
|
||
"alternative: Depend != Related, p_value: 0.00000\n",
|
||
"alternative: Depend < Related, p_value: 0.00000\n",
|
||
"alternative: Depend > Related, p_value: 1.00000\n",
|
||
"Depend & Related, delta: -0.34569, medium\n",
|
||
"alternative: Depend != Subtask, p_value: 0.00000\n",
|
||
"alternative: Depend < Subtask, p_value: 0.00000\n",
|
||
"alternative: Depend > Subtask, p_value: 1.00000\n",
|
||
"Depend & Subtask, delta: -0.08502, negligible\n",
|
||
"alternative: Depend != Triggers, p_value: 0.25516\n",
|
||
"alternative: Depend < Triggers, p_value: 0.12758\n",
|
||
"alternative: Depend > Triggers, p_value: 0.87243\n",
|
||
"Depend & Triggers, delta: -0.03222, negligible\n",
|
||
"alternative: Document != Duplicate, p_value: 0.00000\n",
|
||
"alternative: Document < Duplicate, p_value: 1.00000\n",
|
||
"alternative: Document > Duplicate, p_value: 0.00000\n",
|
||
"Document & Duplicate, delta: 0.07120, negligible\n",
|
||
"alternative: Document != Epic, p_value: 0.00000\n",
|
||
"alternative: Document < Epic, p_value: 1.00000\n",
|
||
"alternative: Document > Epic, p_value: 0.00000\n",
|
||
"Document & Epic, delta: 0.10632, negligible\n",
|
||
"alternative: Document != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Document < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Document > Incorporates, p_value: 1.00000\n",
|
||
"Document & Incorporates, delta: -0.19998, small\n",
|
||
"alternative: Document != Issue split, p_value: 0.00000\n",
|
||
"alternative: Document < Issue split, p_value: 1.00000\n",
|
||
"alternative: Document > Issue split, p_value: 0.00000\n",
|
||
"Document & Issue split, delta: 0.32991, small\n",
|
||
"alternative: Document != Related, p_value: 0.00000\n",
|
||
"alternative: Document < Related, p_value: 0.00000\n",
|
||
"alternative: Document > Related, p_value: 1.00000\n",
|
||
"Document & Related, delta: -0.10106, negligible\n",
|
||
"alternative: Document != Subtask, p_value: 0.00000\n",
|
||
"alternative: Document < Subtask, p_value: 1.00000\n",
|
||
"alternative: Document > Subtask, p_value: 0.00000\n",
|
||
"Document & Subtask, delta: 0.16069, small\n",
|
||
"alternative: Document != Triggers, p_value: 0.00000\n",
|
||
"alternative: Document < Triggers, p_value: 1.00000\n",
|
||
"alternative: Document > Triggers, p_value: 0.00000\n",
|
||
"Document & Triggers, delta: 0.24947, small\n",
|
||
"alternative: Duplicate != Epic, p_value: 0.06194\n",
|
||
"alternative: Duplicate < Epic, p_value: 0.96903\n",
|
||
"alternative: Duplicate > Epic, p_value: 0.03097\n",
|
||
"Duplicate & Epic, delta: 0.01158, negligible\n",
|
||
"alternative: Duplicate != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Duplicate > Incorporates, p_value: 1.00000\n",
|
||
"Duplicate & Incorporates, delta: -0.22259, small\n",
|
||
"alternative: Duplicate != Issue split, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Issue split, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Issue split, p_value: 0.00000\n",
|
||
"Duplicate & Issue split, delta: 0.16253, small\n",
|
||
"alternative: Duplicate != Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Related, p_value: 0.00000\n",
|
||
"alternative: Duplicate > Related, p_value: 1.00000\n",
|
||
"Duplicate & Related, delta: -0.15129, small\n",
|
||
"alternative: Duplicate != Subtask, p_value: 0.00000\n",
|
||
"alternative: Duplicate < Subtask, p_value: 1.00000\n",
|
||
"alternative: Duplicate > Subtask, p_value: 0.00000\n",
|
||
"Duplicate & Subtask, delta: 0.05844, negligible\n",
|
||
"alternative: Duplicate != Triggers, p_value: 0.00002\n",
|
||
"alternative: Duplicate < Triggers, p_value: 0.99999\n",
|
||
"alternative: Duplicate > Triggers, p_value: 0.00001\n",
|
||
"Duplicate & Triggers, delta: 0.11300, negligible\n",
|
||
"alternative: Epic != Incorporates, p_value: 0.00000\n",
|
||
"alternative: Epic < Incorporates, p_value: 0.00000\n",
|
||
"alternative: Epic > Incorporates, p_value: 1.00000\n",
|
||
"Epic & Incorporates, delta: -0.28313, small\n",
|
||
"alternative: Epic != Issue split, p_value: 0.00000\n",
|
||
"alternative: Epic < Issue split, p_value: 1.00000\n",
|
||
"alternative: Epic > Issue split, p_value: 0.00000\n",
|
||
"Epic & Issue split, delta: 0.19551, small\n",
|
||
"alternative: Epic != Related, p_value: 0.00000\n",
|
||
"alternative: Epic < Related, p_value: 0.00000\n",
|
||
"alternative: Epic > Related, p_value: 1.00000\n",
|
||
"Epic & Related, delta: -0.18771, small\n",
|
||
"alternative: Epic != Subtask, p_value: 0.00000\n",
|
||
"alternative: Epic < Subtask, p_value: 1.00000\n",
|
||
"alternative: Epic > Subtask, p_value: 0.00000\n",
|
||
"Epic & Subtask, delta: 0.06053, negligible\n",
|
||
"alternative: Epic != Triggers, p_value: 0.00000\n",
|
||
"alternative: Epic < Triggers, p_value: 1.00000\n",
|
||
"alternative: Epic > Triggers, p_value: 0.00000\n",
|
||
"Epic & Triggers, delta: 0.13168, negligible\n",
|
||
"alternative: Incorporates != Issue split, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Issue split, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Issue split, p_value: 0.00000\n",
|
||
"Incorporates & Issue split, delta: 0.48309, large\n",
|
||
"alternative: Incorporates != Related, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Related, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Related, p_value: 0.00000\n",
|
||
"Incorporates & Related, delta: 0.07553, negligible\n",
|
||
"alternative: Incorporates != Subtask, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Subtask, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Subtask, p_value: 0.00000\n",
|
||
"Incorporates & Subtask, delta: 0.31391, small\n",
|
||
"alternative: Incorporates != Triggers, p_value: 0.00000\n",
|
||
"alternative: Incorporates < Triggers, p_value: 1.00000\n",
|
||
"alternative: Incorporates > Triggers, p_value: 0.00000\n",
|
||
"Incorporates & Triggers, delta: 0.41172, medium\n",
|
||
"alternative: Issue split != Related, p_value: 0.00000\n",
|
||
"alternative: Issue split < Related, p_value: 0.00000\n",
|
||
"alternative: Issue split > Related, p_value: 1.00000\n",
|
||
"Issue split & Related, delta: -0.36514, medium\n",
|
||
"alternative: Issue split != Subtask, p_value: 0.00000\n",
|
||
"alternative: Issue split < Subtask, p_value: 0.00000\n",
|
||
"alternative: Issue split > Subtask, p_value: 1.00000\n",
|
||
"Issue split & Subtask, delta: -0.10871, negligible\n",
|
||
"alternative: Issue split != Triggers, p_value: 0.09849\n",
|
||
"alternative: Issue split < Triggers, p_value: 0.04925\n",
|
||
"alternative: Issue split > Triggers, p_value: 0.95077\n",
|
||
"Issue split & Triggers, delta: -0.05786, negligible\n",
|
||
"alternative: Related != Subtask, p_value: 0.00000\n",
|
||
"alternative: Related < Subtask, p_value: 1.00000\n",
|
||
"alternative: Related > Subtask, p_value: 0.00000\n",
|
||
"Related & Subtask, delta: 0.22655, small\n",
|
||
"alternative: Related != Triggers, p_value: 0.00000\n",
|
||
"alternative: Related < Triggers, p_value: 1.00000\n",
|
||
"alternative: Related > Triggers, p_value: 0.00000\n",
|
||
"Related & Triggers, delta: 0.30595, small\n",
|
||
"alternative: Subtask != Triggers, p_value: 0.03715\n",
|
||
"alternative: Subtask < Triggers, p_value: 0.98143\n",
|
||
"alternative: Subtask > Triggers, p_value: 0.01857\n",
|
||
"Subtask & Triggers, delta: 0.05442, negligible\n",
|
||
"------------------------------\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1800x900 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 2000x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 2000x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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YMEAvvfSSzyS4w+HQgAEDTIgKQF1S3KfevHlzidkkHQ6HOnXqRJ+6kmyGH2Pn27RpE/D6TTabTd9++21A57CirKwsSVJsbKzJkQDBJTMzUxMmTChV/swzzzBlI4AaQTsEAAgGc+fO1fz582UYhmw2m2677TbdeeedZocFC8jJydFdd91Vqnzu3LksxYAKow0CYKYVK1bo2WefLVX+yCOPqHfv3iZEBKCuycnJ0dixY0s8jBMSEqLXXnuNPvVp/Mm1VmoKdMMwAvqvpm3atElJSUm66qqr1L59e1111VUaPXq01qxZU2rfzMxM/e1vf1N8fLwuv/xyDRo0SHPnzmWtD8BiWHcXQE1h3VQAgNXl5OTI5XJ5v68bhiGXy6WcnByTI4MVbN++3Wf5tm3bajgSWBVtEACz7d6922f5L7/8UsORAKiroqOj5XQ6SyxL5XQ6SX4HoFIjwKOjo3XLLbeof//+lZqOKDo62u9jKuull17Siy++qEaNGqlXr15q0qSJDhw4oG+//VbdunXTww8/7N135cqVGj9+vOrVq6cbbrhBkZGR+vzzz7Vz507169dP06dPr3QcjAAHqp7H49GwYcPKnHp4wYIFstsr9ZwP6qC0tDTvtLE9evQwOxxYzKFDh+R0OuXxeGS32+VyuRQZGWl2WAAAlMswDE2aNElbtmwp8eC3w+FQx44dlZycHPBMcAhebrdbN954Y5nTxn788cdyOBwmRAaroA0CYLbCwkINHDiwzO1Lly5VaGhoDUYEoK4qKCjQ6NGjtX//fjVu3Fhz5sxhSZgz+JNr9WsN8OHDh2vZsmXas2ePZsyYoTlz5mjAgAG69dZba+Uop2XLlunFF1/UFVdcoZSUFJ177rklthcWFnp/PnLkiCZPniy73a633nrL+8v7xz/+oTvvvFPLly/X0qVLz/phCKBmpaen+0x+S1J+fr7S09PVvXv3Go4KVsR6cwhUZGSkEhISlJqaqoSEBJLfAADLyM7OVkZGRqlyt9utjIwMZWdnq2XLliZEBitYtmxZmTPmud1uLVu2TIMGDarhqGAltEEAzDZr1qxyt48fP76GogFQl4WHhyspKck7SIv704Hxa2jklClTtHbtWj333HPq0aOHjh8/roULF2r48OG64YYb9Nprr2nfvn3VFatfPB6Pnn32WYWHh+u5554rlfyWVOLJrU8++UQHDhzQjTfeWOLJgXr16un++++XJM2bN6/6AwdQYfHx8YqIiPC5LSIiQvHx8TUcEawqNTVVeXl5kqS8vDy5XC6TI4IVjRo1Sp9++qlGjRpldigAAFRYTEyM4uLiSs2c5HA41KVLF8XExJgUGaxgwIABZY7wdjgcGjBgQA1HBKuhDQJgtvvuuy+g7QBQlXr06KF33nmHGUqrgN9zA4eFhWngwIGaM2eOVq9eraSkJEVHR2vnzp167rnn1KtXL91777367LPPVFRUVB0xV0hmZqZycnJ0zTXXKCIiQl988YVeffVVvfnmm9q8eXOp/Tds2CBJuvrqq0tt69q1q8455xxt2bJFJ0+erPbYAVSM3W7XpEmTfG4rntEBKA/rzQEAgLrMZrMpMTGx1BTDZZUDp3M4HHrggQd8bnvwwQeZ/hzlog0CYLbQ0FDdcMMNPrcNHDiQ6c8BwKL8mgL9TBdccIHGjRuncePGacOGDXr//ff12Wef6YsvvtCaNWt03nnnafDgwbrtttvUqlWrqoq5QrZt2yZJatKkiYYMGaLvv/++xPauXbtq+vTpOv/88yVJO3fulCSfcYaEhKhFixb68ccflZ2drYsvvrhSMRmGoWPHjlXqWAC+tWnTRpdddpl27NjhLWvbtq0uvfRS/t5QLsMwNH36dG/yu5jH49H06dP1n//8hxsuAAAg6DVq1EhDhgzRe++9J8MwZLPZNGTIEJ133nn0qVGuq666Sm+88YZ3RiVJaty4sXr06EH9QYXQBgEwk2EY+v33331u+/XXX3X06FHuDQFALVHcV6yIgBLgp+vWrZu6deumI0eO6JNPPtH777+vLVu26K233pLD4dDDDz9cVZeqkOIvXvPnz1dMTIzefPNNxcbG6tdff9VTTz2ldevW6f7779fbb78t6dQa4JLUsGFDn+crnkK9rPWGK6KwsLBEkg5A1RgyZIieeOIJ779vueUW/tZQIbm5uT5nBfF4PNq8ebO+/PJLNW3a1ITIAAAAalb79u21fPly5efnKyIiQu3ataNPjQobM2aMnnnmGe+/R48eTf2BX2iDAJilrHtDkrg3BL/t2LFDH374oQYPHqzLLrvM7HCAoBQWFlah/aosAV7s3HPPVYsWLRQdHa1t27bJ7XZX9SUqpPi6xaP7WrduLUn6y1/+opkzZ6pfv35KT0/X5s2b1alTpwqfN5CnvUJDQ3XJJZdU+ngAvu3bt6/Evy+88EI1adLEpGhgJW3atNEXX3yhb775Rh6Px1tut9t1+eWXq2fPnjzlCwAA6ozExETNnj1bf/vb33T55ZebHQ4s5MSJEwoLC9PJkycVFhamuLg41atXz+ywYDG0QQDMUHxvaMuWLSVmCLTZbOrYsSP3hlBhJ06c0H//+18dPHhQS5cu1cCBA+kPAVXsp59+qvC+VZYA37NnjxYvXqzFixfrt99+k2EYCgkJ0bXXXqv+/ftX1WUqLDIyUpIUExPjTX4XCw8P11VXXaX33ntPW7duVadOnbwjvA8fPuzzfOWNEK8Im82m+vXrV/p4AL5NnDixxL8nTZqkd99916RoYDXjx4/X2LFjS5TZ7Xbdf//9atCggUlRAQAA1Lxrr71W1157rdlhwIIWLFigwsJCSadmv/vwww915513mhwVrIY2CIBZxo8fr7vuuqtEmWEY3BuCXxYsWKADBw5Ikv744w/6Q0A18OeBJHsgFzpx4oQ++OAD3XHHHerbt69eeukl/frrr/rzn/+shx56SF988YVmzZqlDh06BHKZSrnooosklZ2wjoiIkHTqNZy+/65du0rtW1RUpD179igkJEQxMTHVEC2AylqxYoX2799fomzfvn1asWKFSRHBaqKjo+V0Or0fnjabTU6nU82bNzc5MgAAAKD2y8nJkcvl8o6aMwxDLpdLOTk5JkcGAEDFbN++3Wf5tm3bajgSWBX9IaD2qVQCfMuWLXr00Ud15ZVXasKECUpPT1f9+vU1dOhQuVwuLV26VGPGjFHjxo2rOt4K69Kli0JCQvS///1PJ0+eLLX9xx9/lHQq8SFJ3bt3lyStXbu21L4bN27U8ePH1alTpwrPLQ+g+rndbj3//PM+tz3//POmLcEA60lISFBUVJQkKSoqSk6n0+SIAAAAgNrPMAzNmDGjzPLTp5IFAKA24v4iAkV/CKid/EqAz549WzfccIOGDx+uhQsX6ujRo4qPj9czzzyj9evX6/HHH681a/Scf/75uuGGG5Sfn69XXnmlxLb169dr3bp1atiwoa6++mpJUv/+/dWoUSMtXbpUWVlZ3n1PnDihF198UZI0fPjwmnsBAMq1bNmyMjuhbrdby5Ytq+GIYFXh4eHq27ev7Ha7+vbtq/DwcLNDggWlpaVpxIgRSktLMzsUWBR1CIDZaIfgr+zsbGVkZJT6XuZ2u5WRkaHs7GyTIoMVzZ07V/3799fcuXPNDgUWxecYKoP7iwgU/SGgdrIZfjx+0qZNG9lsNkVHR+vmm2/WkCFDavUUsXl5eRo+fLh2796trl27KjY2Vjk5OVq5cqVsNpueffZZ3XDDDd79V65cqfHjx6tevXoaMGCAIiMjtXr1au3cuVP9+vXTiy++6Nf88qcrTqrHxsZWyWsDcKoTceONN/rspDocDn388cdyOBwmRAarKSgo0OjRo7V//341btxYc+bMIQkOv1CHECjqEACz0Q6hMgzD0KRJk5SRkVGi3GazKS4uTk888USl76Ogbjl06JCcTqc8Ho/sdrtcLpciIyPNDgsWwucYKov7iwhUcX9o8+bN8ng83nKHw6FOnTrRHwKqkD+51kpNgZ6Tk6OZM2eqd+/euuyyy/z6r23btpW5ZKVERUVpwYIFGjVqlH799Ve9/fbb2rBhg6655hq9++67JZLfktSnTx+9/fbb6tKli1asWKF33nlHoaGhmjhxop5//nkaKaCWcTgceuCBB3xue/DBB+mcosJSU1OVl5cn6dTDUy6Xy+SIYDXUIQSKOgTAbLRDqAybzabExMRS5YZhKDExkfsoqLCpU6d6kwYej0ePPfaYyRHBavgcQ2VxfxGBKu4PndnvKascQM3wOwFuGEbA/9Wk8847TxMnTtTq1au1bds2bdiwQbNmzVLHjh197h8XF6fZs2dr48aN2rp1qz766CONGjWKDzrAYlifBxWVk5Mjl8vl/XwyDEMul0s5OTkmRwaroA4hUNQhAGajHUIg9u7d67P8999/r+FIYFWZmZnavn17ibJt27YpMzPTpIhgNXyOIVC7d+/2Wf7LL7/UcCSwqujoaDmdTm+y22azyel01uoZlIFg59cU6IsXL66Si95yyy1Vch4rYQp0oOoxRRECVTxF0ZYtW0rUI4fDoY4dOyo5OZmnNHFW1CEEijoEwGy0QwiEx+PRsGHDlJ+fX2pbRESEFixYILu9UpMPoo6gDiFQfI4hUIWFhRo4cGCZ25cuXarQ0NAajAhWxVIMQPXzJ9ca4s+J62LiGkDttWzZsjJHervdbi1btkyDBg2q4ahgJdnZ2aXWK5RO1Z+MjAxlZ2erZcuWJkQGq6AOIVDUIQBmox1CINLT030mLiUpPz9f6enp6t69ew1HBSuhDiFQfI4hULNmzSp3+/jx42soGlhZeHi4kpKSNHPmTI0bN47kN2AyHqEEYFkDBgwoc4S3w+HQgAEDajgiWE1MTIzi4uJ8rtHTpUsXxcTEmBQZrKK4Dp05KsXhcFCHUCHUIQBmK26HfKEdQnni4+MVERHhc1tERITi4+NrOCJYDXUIgaI/jUDdd999AW0HTtejRw+988476tGjh9mhAHUeCXAAluVwOPTAAw/43Pbggw8y/TnKZbPZNHToUJ25GohhGBo6dCjTpKFcNptNiYmJPh+i8FUOnIk6BMBsNptNvXr18rmtV69etEM4K7vdrkmTJvncNnnyZKauRrmoQwgU/WkEKjQ0tMypdGNjY5n+HAAsKuBeZFZWll577TU98sgjuueee3TPPffokUce0ezZs71zsQNAdenbt68aN25coqxJkybq3bu3SRHBSgzD0MKFC31+UV6wYEGpxDjgS3R0tJxOp7ce2Ww2OZ1ONW/e3OTIYBXUIQBm8ng8evXVV31ue+WVV+TxeGo4IlhN586d1a5duxJl7du3V8eOHc0JCJZDHUKg6E8jEEVFRWXmMbKyslRUVFTDEQEAqkKlE+AbN27U0KFDNWzYMD333HP68MMP9eWXX+rLL7/Uhx9+qGnTpmnYsGEaOnSo0tPTqzJmBKG0tDSNGDFCaWlpZocCC3rhhRdK/Pv55583JxBYTvFaYb5GgBevFQZUREJCgvep8NDQUDmdTpMjgtUkJCQoKipKkhQVFUUdAlBjKrL+LlCeqVOnlvj3lClTzAkEljV16tQSyUvqEPxFfxqV9e677wa0HTgdeQ6g9qhUAnzu3LkaNWqUtm3bJsMwZBiGQkJCFBUVpfPPP18hISHe8qysLN11112aM2dOVceOIFFQUKCUlBTl5uYqJSVFBQUFZocEi4mIiFBYWJgkKSwsrMz1w4AzlbVWmN1uZ60w+CU/P18nT56UJJ08ebLMRAJQlvDwcCUlJalp06ZKSkpSeHi42SEBqCNYfxdV4cy+D30h+KtevXre/k94eLjq1atnckSwGvrTqKzbb789oO1AMfIcQO3idwL8ww8/1FNPPSW3263zzz9fSUlJ+uijj7R161atW7dO69ev19atW/Xhhx8qMTFRUVFRcrvd+u9//6sPPvigOl4DLC41NVV5eXmSpLy8PLlcLpMjgtWkpqaqsLBQklRYWEgdQoUVrwnmawQ4a4XBH//4xz9K/PuBBx4wJxBYWo8ePfTOO++oR48eZocCoA5h/V1UhcTExBL/TkpKMikSWFVqaqo3UVBQUMD3elQK/WlURkhIiMaMGeNz2913362QkJAajghWRZ4DqF38+iZ7+PBhPfHEE7LZbLryyiu1dOlSjRs3Tn/5y19KJAlsNpsuvfRSJSYmaunSpbriiitkGIaSk5N1+PDhKn8RsK6cnBy5XC5v8skwDLlcLuXk5JgcGayCOoTqwvrfqKgVK1Zo//79Jcr27dunFStWmBQRAAD+Yf1dBMLlcun48eMlyo4dO8ZNX1QY3+sB1FZut9vsEGARfJYBtY9fCfCFCxfq0KFDuuSSSzRr1iydd9555R5z3nnn6eWXX9Yll1yi/Px8LVy4sLKxIsgYhqEZM2aUWU7yCeWhDiFQxXXlzJHeNpuNOoQKcbvdev75531ue/755/myDACwDNbfRWUUFRXp9ddf97nt9ddfV1FRUQ1HBKvhez0As/FZhkDxWYaqxDryVcevBPiXX34pm82m8ePHe9fbrYiwsDCNHz9ehmFozZo1fgeJ4JSdna2MjIxSyQG3262MjAxlZ2ebFBmsgjqEQBXXIY/HU6Lc4/FQh1Ahy5YtKzPJ7Xa7tWzZshqOCACAymH9XVTGu+++G9B2gO/1AMzGZxkCxWcZqgrryFctvxLgP//8s2w2m3r27On3ha655hrZbDb98ssvfh+L4BQTE6O4uLhSa8o5HA516dJFMTExJkUGqyiuQ75Qh1ARtEMI1IABA+RwOHxuczgcGjBgQA1HBABA5bD+Lirj9ttvD2g7UNb3epvNxncyADWCzzIEqvizzNcMk3yWwR+sI1+1/EqAHzx4UA0bNqzUk+D16tVTRESEDh486PexCE42m02JiYk+Pxh8lQNnstlsZa5L2LFjR+oQykU7hEA5HA498MADPrc9+OCDZSbHAQCoTVizEJUVEhKiG264wee2gQMHKiQkpIYjgtXYbDYNHTq0VLlhGBo6dCjfyQBUu5CQEF166aU+t1166aV8lqFcxZ9lZ051zmcZ/MF3sqrnVwK8fv36Onr0aKUvdvToUdWvX7/SxyP4REdHy+l0llhrzul0qnnz5iZHBitwu92aO3euz21vvPEGa++iQmiHEKi+ffuqcePGJcqaNGmi3r17mxQRAAAVx5qFCITH49H69ev/P/buPj6K8t7//3t3c7MECEq4UWi8bb0poki4KV9bW44UId7VG5pYRFSUFg0oCsdC6VG+xXA8Kqkm9R4FEZslp61YTZTTHhXtl9PQgAp4g221xlS5iUoIZJPs7vz+8CQ/IbPZhCuZyWxez8eDP5h3wn4gw8w1c81cH9vstddea9NqCDicZVkqKyuzfSh53bp1HIMAdLumpibt3LnTNtu5c6eampocrghew7kMprgm6x6dmgA/5phjFI1GtWPHjk5/0I4dOxSJRDR06NBOfy+SW35+vrKysiRJWVlZysvLc7kieAW9d9FVOA7B1C9/+ctDfl9UVOROIQAAdBI9C2GisrJSdXV1tlldXZ0qKysdrghe03IMsntrjmMQACfcc889RjnAuQymuCbrHp2aAB83bpwsy9JTTz3V6Q9avXq1fD6fxo8f3+nvRXILBoOaO3euhgwZorlz5yoYDLpdEjyC3rvoKhyHYCozM1NpaWmSpLS0NGVmZrpcEQAAHdPSs9DvP/T2QCAQoGchEho3blzccU9mZqbGjRvncEXwGo5BANy2cOFCoxzgXAZT7EPdo1MT4Jdddpl8Pp+ee+45lZWVdfj71q1bp+eee06SdOmll3auQvQKEyZM0NNPP60JEya4XQo8hN676Eoch2CitLRUzc3NkqTm5maFQiGXKwIAoGN8Pp8KCgpsl2y02w58ld/v1+LFi22zJUuWtLmJBxyOYxAAt6WlpWnq1Km22QUXXND6sDsQD+cymGIf6h6duhI5/fTTdckll8iyLP3bv/2bfvazn7XbgL2mpkaLFy/WHXfcIZ/Pp4suukjf/OY3jYsGgBYjRoyw3X7aaac5XAmA3qqmpkahUKh1qSvLshQKhdodIwEA0JMMHz5ceXl5rTdWfD6f8vLyNGzYMJcrgxeMHj26zXXZGWecoVGjRrlTEDyHYxAAt8W7v3j66ac7XAm8inMZTLEPdT2f1cnu6Y2NjZoxY4beeust+Xw++f1+nX766frmN7+po446Sj6fT59//rl27Nihd955R5ZlybIsjRw5Uk8//bTS09O76+/So23btk2SNHLkSJcrAZKHZVlavHixtmzZckiPFZ/Pp9GjR6uwsJCnowB0q5bj0BtvvHFIn55AIKBRo0ZxHAIAeEY4HNZ1112nvXv3atCgQXriiSdoC4MO27dvn6ZNmybpy+uxdevWacCAAS5XBS8Jh8OaPn269u/fr/79+2vt2rUcgwA4IhqN6sILL2zTe1f68tr++eefZ5VJdEg4HFZeXp4aGhqUkZGh0tJSzmXoFK7JEuvMXGun16JKT0/XU089pUsvvVSWZSkajWrHjh0qKyvTY489pkcffVRlZWV6++23FYvFZFmWfvCDH+ipp57qtZPfALpHdXW1qqqqdPhzPJZlqaqqStXV1S5VBqC3aDkOHX6hHI1GOQ4BADwlGAxq7ty5GjJkiObOncuNFhyxTr5nAQCAq8rLy20nv6Uvr+3Ly8sdrghe1djYqHA4LElqaGhQY2OjyxXBa7gm61pH1IwpGAxq+fLleu6555Sfn6/jjz++9U3vll/HHXec8vPztX79ev37v/+7+vTp09W1A+jlsrOzlZOTY5uNGTNG2dnZDlcEoLdpOQ4d3t8yEAhwHAIAeM6ECRP09NNPa8KECW6XAo+58847D/n90qVL3SkEnlVaWqr6+npJUn19vUKhkMsVAegtcnNz477hHQgElJub63BF8Ko777zzkPZ4jIdwJLgm6zopJt98yimntF7kRCIR7du3T5I0YMAApaQY/dEAkJDP59OoUaNUVVXVJhs1ahTLDgPodj6fTwUFBbr++uttt3McAgAAyW7Lli3asWPHIdu2b9+uLVu2aPTo0S5VBS+pqalRKBQ6ZNIgFApp0qRJGj58uMvVAUh2gUBA55xzjjZu3NgmO+ecc1j+HB3CeAjoeY7oDXA7KSkpysrKUlZWFpPfABwRjUa1atUq2+zJJ5+Mu3wRAHSl4cOHKy8vr3Wy2+fzKS8vT8OGDXO5MgAAgO4Vi8VUWFhomxUWFioWizlcEbzGsiyVlJTE3c6S+gC6WyQSsZ38lqSNGzcqEok4XBG8hvEQ0DN1egL8iy++0Lvvvqv33nuvQ19vWZbee+89vfvuu61viANAV6BHD7rSpk2bdNVVV2nTpk1ulwIPys/PV79+/SRJ/fv3V15enssVAQAAdL/KykrV1dXZZnV1daqsrHS4InhNdXW1qqqq2lzbR6NRVVVVqbq62qXKAPQWa9euNcoBxkNAz9TpCfCbb75Zl156qZ555pkOfb3P51NpaakuvfRSLVy4sNMFAkA89OhBVwmHwyouLtbu3btVXFyscDjsdknwMN5SAQAAvcW4ceOUmZlpm2VmZmrcuHEOVwSvyc7OVk5Ojvz+Q29RBgIBjRkzRtnZ2S5VBqC3mD59ulEOMB4CeqZOTYC/9dZb+vOf/6ysrCwtXry4w9/305/+VFlZWXrttdf0zjvvdLpIALATCAQ0f/5822zBggX06EGHlZaWqra2VpJUW1urUCjkckXwmtLSUtXX10uS6uvr2YcAAECv4Pf7494fWrJkSZtJTeBwPp9PBQUFre2EEm0HgK6WkpKiWbNm2WazZ8+m3SsSYjwE9Eyd+p/3/PPPy+fz6eqrr1Z6enqHvy89PV0zZ86UZVl67rnnOl0kAHQW/b/RUTU1NQqFQq1v7VqWpVAopJqaGpcrg1ewDwEAgN5s6NChttsHDx7scCXwquHDhysvL691stvn8ykvL0/Dhg1zuTIAvR33F9FRo0eP1ogRIw7ZdsYZZ2jUqFHuFASgcxPgW7dulSSdd955nf6glu+pqqrq9PcCgJ1oNKqioiLbrKioiEEqErIsSyUlJXG3s5Q1EmEfAgAAvVnLmMfu7V3GQuiM/Px8ZWVlSZKysrKUl5fnckUAeotIJKKVK1faZitXrlQkEnG4InjVnXfe2fq2t9/v1x133OFyRUDv1qkJ8I8++kiBQEAnn3xypz/opJNOUkpKij766KNOfy8A2CkvL487yR2NRlVeXu5wRfCa6upqVVVVtdmPotGoqqqqVF1d7VJl8Ar2IQBAMlm1apWmTJmiVatWuV0KPKJlLHT4RLdlWYyF0CnBYFBz587VkCFDNHfuXAWDQbdLAtBLrF271igHWgwYMECnn366JOn000/XgAEDXK4I6N06NQF+4MAB9evX74g/rG/fvq39MQHAVG5ubtw+34FAQLm5uQ5XBK/Jzs5WTk5Om148gUBAY8aMUXZ2tkuVwSvi7UN+v599CADgKfv27VNpaalisZhKS0u1b98+t0uCB7SMhQ7n8/kYC6HTJkyYoKeffloTJkxwuxQAvcj06dONcqDFvn379M4770iS3nnnHcbTgMs6NQFuOoF94MAB9e3b94i/HwC+KhAIaP78+bbZggUL4k6OAy18Pp8KCgpsl2y02w4crmVficVih2yPxWLsQwAAT7nzzjtbz2exWExLly51uSJ4QctY6HCWZTEWAgB4QkpKimbNmmWbzZ49WykpKQ5XBK9iPA30LJ2aAB88eLCi0ah27tzZ6Q96//33FYlENGjQoE5/LwB0Fv2/0VHDhw9XXl5e6805n8+nvLw8DRs2zOXK4BW7du2y3f7pp586XAkAAEdmy5Yt2rFjxyHbtm/fri1btrhUEbyEsRAAIFlxfxEdxXga6Hk6NQE+evRoSTqivrrPP//8IX8GAJiKRqMqKiqyzYqKihikosPy8/NbW3z069dPeXl5LlcEr4jFYiosLLTNCgsL27wZDrRn2bJlmjx5spYtW+Z2KfCoTZs26aqrrtKmTZvcLgUewrkMJth/AABeF4lEtHLlStts5cqVikQiDlcEr2E8BPRMnZoAnzRpkizL0lNPPaW//e1vHf6+v/71r3rqqafk8/k0adKkThcJAHbKy8vjTnJHo9EjelgHADqjsrJSdXV1tlldXZ0qKysdrghetXv3bm3cuFGStHHjRu3evdvliuA14XBYxcXF2r17t4qLixUOh90uCR7BuQwm2H8AAF63du1aoxxgPAT0TJ2aAD/33HM1YsQIHTx4UNddd52qqqoSfs9f/vIXzZo1S+FwWN/85jf13e9+94iLBYCvys3NjdvnOxAIKDc31+GK4FWlpaWqr6+XJNXX1ysUCrlcEbxi3LhxyszMtM0yMzM1btw4hyuCV91yyy2H/H7+/PnuFALPKi0tVW1trSSptraWcxk6jHMZTLD/AAC8bvr06UY5wHgI6Jk6NQEuSffee68yMzO1e/duXXXVVbruuuv0zDPP6I033tAHH3ygDz/8UG+88YaeeeYZXXfddZoxY4Z27dqlfv366Z577umOvwOAXioQCOiyyy6zza644oq4k+PAV9XU1CgUCsmyLEmSZVkKhUKqqalxuTJ4gd/v17Rp02yzvLw8+f2dHmqhF9qwYYP27t17yLY9e/Zow4YNLlUEr+FcBhN+v1+zZ8+2zX7yk59wLkO72H8AAF6XkpKi4cOH22Zf+9rXlJKS4nBF8BruDQE9U6f/55144ol6/PHHNWTIEFmWpU2bNukXv/iFrrzySuXm5mrq1Km68sor9Ytf/EKbNm2SZVkaMmSIHn/8cZ100knd8XcA0EvFYjG99NJLtllFRQX9VZCQZVkqKSmJu71lIgGIJxaLqayszDYLhUIch5BQNBpVUVGRbVZUVBS31QfQgnMZTFmWpZdfftk2++Mf/8g+hHax/wAAvC4cDsd9cPTjjz+mtRAS4t4Q0DMd0aMnZ555ptavX69Zs2apX79+sizL9le/fv00a9YsrV+/XmeddVZX1w6gl6O/CkxVV1erqqqqzQRTNBpVVVWVqqurXaoMXsFxCKbKy8vjTnJHo1GVl5c7XBG8hnMZTLXsQ3bYh5AI+w8AwOsWLVpklAPcGwJ6piNev+Ooo47SwoULdeutt2r79u16//339cUXX8iyLB199NH6xje+oTPOOIMliAF0m5b+KnYDDPqroCOys7OVk5OjrVu3HvI0ZiAQ0Nlnn63s7GwXq4MXcByCqdzcXD344IO2k+CBQEC5ubkuVAUv4VwGUy370JYtWw55W9fv92v06NHsQ2gXxyAAgNctX75cF198cbs50B7uDQE9k3HzgUAgoLPOOktXXHGFrr/+et1www264oordNZZZ3V48ruiokLPPvusaSkAehm/36/FixfbZkuWLKG/ChLy+XwqKCiQz+fr0HbgcByHYCoQCGj+/Pm22YIFC3iYFAlxLoOpln3lcJZlsQ8hIY5BAACvCwaDGjt2rG02fvx4BYNBhyuC13BvCOiZesT/vLvuuivuAQIA2rN3717b7bt373a4EnjV8OHDlZeX13pzzufzKS8vT8OGDXO5MnjF0KFDbbcPHjzY4UrgVZMnT9agQYMO2TZ48GCdd955LlUEr+Fchu5C/2Z0BMcgAIDXffe737Xd/p3vfMfhSuBV3BsCep4eMQEucWENoPOi0aiKiopss6Kiorg9VYHD5efnKysrS5KUlZWlvLw8lyuCV1iWpZKSkjZP8/r9fpWUlDC+QYf98pe/POT38c5vQDycy3CkWs5ldm/wci5DR+Xn56tfv36SpP79+3MMAgB4BvcXYYrxNNAz9ZgJcADorPLy8riD0Gg0qvLycocrglcFg0HNnTtXQ4YM0dy5c1neCh1WXV2tqqqqQ3peSlIsFlNVVZWqq6tdqgxeM2TIEJ177rmSpHPPPVdDhgxxuSJ4DecyHCnOZehq3OQFAHgJ9xdhqmU8ffgYyLIsxtOAi5gAB+BZubm5cXujBgIB5ebmOlwRvGzChAl6+umnNWHCBLdLgYdkZ2crJyfHNhszZoyys7MdrghetmTJEm3YsEFLlixxuxR4FOcyHAnOZegKpaWlqq+vlyTV19crFAq5XBEAAB3D/UWYYjwN9ExMgAPwrEAgoG9+85u22Te/+c24g1cA6Co+n0+jRo2yzUaNGtVm+SsAAHoan8+niRMn2mYTJ07kXIaEampqFAqFWt96sixLoVBINTU1LlcGAEBi3F+EKe4NAT0TE+AAPKupqUnbtm2zzbZt26ampiaHKwLQ20SjUa1atco2e/LJJ+kVBgDo8WKxmB599FHb7JFHHmmzNDrwVS09L+NtZzl0AEBPx/1FmOLeENAzMQEOwLPuueceoxz4qk2bNumqq67Spk2b3C4FHkKvMHQljkMA3FBZWam6ujrbrK6uTpWVlQ5XBC9p6Xl5+HgoGo3S8xIA4AncX4Qp7g0BPRMT4AA8a+HChUY50CIcDqu4uFi7d+9WcXGxwuGw2yXBI+gVhq7CcQiAW8aNG6fMzEzbLDMzU+PGjXO4IngJPS8BAF7H/UWY4t4Q0DMxAQ7As9LS0jR16lTb7IILLlBaWprDFcGrSktLVVtbK0mqra1VKBRyuSJ4RSAQ0Pz5822zBQsW0CsMHcZxCIBb/H6/Fi9ebJstWbJEfj+3DRCfz+dTQUGBbVZQUEDPSwBAj8f9RZji3hDQM3ElC8DTRowYYbv99NNPd7gSeFVNTY1CoVBrf0LLshQKhVRTU+NyZfA6ejyhozgOAXDb6NGj24yrzzjjDI0aNcqdguApO3bssN2+fft2hysBAODI9OvXz3Z7RkaGw5XAq+Ldoz7ttNMcrgRACybAAXhWNBpVUVGRbVZUVMTkExKyLEslJSVxt7dMRgHxcByCKY5DAHqKO++8s/Vtb7/frzvuuMPliuAFjIUAAF7X3NyssrIy26ysrEzNzc0OVwSviXddL4nresBFTIAD8Kzy8vK4N1Si0ajKy8sdrgheU11draqqqjb7UTQaVVVVlaqrq12qDF7BcQimOA4B6CkGDBig/Px8+f1+5efna8CAAW6XBA9gLAQA8LqHHnrIKAdaruvtcF0PuKdHTIBPmTJFP/jBD9wuA4DH5Obmxu2hEggElJub63BF8Jrs7Gzl5OTYZmPGjFF2drbDFcFrOA7BVMtx6PAeu4FAgOMQAMddc801evHFF3XNNde4XQo8grEQAMDr5syZY5QD8e4v+nw+rusBF/WICfAlS5Zo+fLlbpcBwGMCgYAmT55sm51//vlxb8QALXw+nyZOnGibTZw4UT6fz+GK4DWBQEDnnHOObfbtb3+b4xAS8vl8KigoaHO8ibcdAICeJBAIxH1g4rrrrmMsBADo8VJTUzV06FDbbOjQoUpNTXW4IniNz+fTtGnT2my3LEvTpk3juh5wSacmwP/5z392yS8A6ArRaFQbNmywzV566SX6zSGhWCymRx991DZ75JFHFIvFHK4IXhOJRLRx40bb7NVXX1UkEnG4InjR8OHDlZeX13pR7PP5lJeXp2HDhrlcGQAA7bMsS2+88YZttmXLFnpeAgB6vHA4rF27dtlmu3btUjgcdrgieI1lWSorK7N9sH3dunWMhwCXpHTmi//lX/7F+GkVn8+nt99+2+jPAACpY/3mLrroIoergpdUVlaqrq7ONqurq1NlZaW+9a1vOVwVvGTt2rUJ85kzZzpUDbwsPz9fv//977V//37169dPeXl5bpcED1q1apVKS0uVn5/PEtY4Ips2bdKvfvUr3XTTTZowYYLb5cADOtLz8rjjjnO4KgAAOm7RokUJ86KiIoeqgRfFGw9ZlsV4CHBRp5dAtyzL+BcAdAX6zcHUuHHjlJmZaZtlZmZq3LhxDlcEr5k+fbpRDgBdZd++fSotLVUsFlNpaan27dvndknwmHA4rOLiYu3evVvFxcW87YQOidfzUhI9LwEAnpCoNSutW5EI4yGgZ+rUG+DSl29wDx8+XJdeeqnGjh3bHTUBQIcEAgHNnz9f9957b5tswYIF9JtDQn6/X4sXL9ZPf/rTNtmSJUvk93f6OTH0MikpKZo1a5ZWrlzZJps9e7ZSUjo91EIvVVpaqvr6eklSfX29QqEQqwegU+68887W1h2xWExLly7VihUrXK4KXlJaWqra2lpJUm1tLcchdIjP51NBQYGuvfbaNllBQQE9LwEAPV4wGNTYsWO1efPmNtn48eMVDAZdqApewngI6Jk6dVf23HPP1Z/+9Cd9/PHHKikpUXZ2ti677DJdeumlGjp0aHfVCACdRv9vdFS889fgwYMdrgRelZeXp2eeeUYNDQ2t2zIyMnTFFVe4WBW8pKamRqFQqHWlJMuyFAqFNGnSJA0fPtzl6uAFW7Zs0Y4dOw7Ztn37dm3ZskWjR492qSp4CcchmDj8+NNi+/btGjZsmMPVAADQeX369LHdnp6e7nAl8CrGQ0DP06lX2x599FG9/PLLuvXWW3X88cfro48+0v33369/+Zd/0fXXX6+Kigo1NTV1V61IYps2bdJVV12lTZs2uV0KPCQajcbtwVNUVMQkOBKyLEslJSVt3vT2+/0qKSmhbQc6rKSk5JDfFxcXu1QJvKblOBRvO8chJBKLxVRYWGibFRYWtr4VDsTDcQgmuCYDAHhdY2OjNm7caJtt3LhRjY2NDlcEr2E8BPRMnV7bdciQIZo9e7ZefPFFrV27VpdeeqnS09P1+uuv69Zbb9V3vvMdLVu2LO4TL8Dh6DWHI1VeXh53ABGNRlVeXu5wRfCa6upqVVVVtZkciMViqqqqUnV1tUuVwWsO7yUfr7c8cLiW49Dh57NoNMpxCB1SWVmpuro626yurk6VlZUOVwSv4TgEE1yTAQC8bunSpUY5wHgI6JmMmpvm5OSosLBQf/rTn3TXXXdp9OjR2rdvn55++mldccUVuvjii/XUU0/p888/76p6kYTses0BHZGbmxu3z3cgEFBubq7DFcFrsrOzlZOT0+YN8EAgoDFjxig7O9ulyuA1d9555yG/5wIZHcVxCKbGjRsX96GbzMxMjRs3zuGK4DUtxyE7HIeQCNdkAACvu+OOO4xygPEQ0DMZTYC36NOnjy6//HKtXbtWGzZs0I9//GMNHTpUO3fu1PLly/XYY491xccgCcXrNVdTU+NyZfCCQCCgc845xzb79re/HXfgAbTw+XwqKCiQz+fr0HbATnu9d4FEOA7BlN/v1+LFi22zJUuWtHm4Ajicz+fTxIkTbbOJEydyHEK7AoGArrnmGtvsuuuu45oMANDjpaenq2/fvrZZv3796AOOhAKBgC677DLb7PLLL2c8BLiky++GHHfccbrssst04YUXKjU1tav/eCQRes3BVCQSiduj59VXX1UkEnG4InjR8OHDlZeX13pz1+fzKS8vT8OGDXO5MngBvXfRFTgOwdTo0aM1YsSIQ7adccYZGjVqlDsFwVNisZgeffRR2+yRRx7hXIZ2WZalN954wzbbsmUL1/UAgB7v4MGDOnDggG1WX1+vgwcPOlwRvCYWi+mll16yzV588UXG04BLumwCvKGhQb/97W81ffp0TZkyRStXrlRzc7O+8Y1vaMKECV31MUgi9JqDqbVr1xrlQIv8/HxlZWVJkrKyspSXl+dyRfAKeu+iq+Tn56tfv36SpP79+3McQqfdeeedrW97+/1+lmpEh3Eug4mW63o7XNcDALxg3rx5RjnAeBromYwnwP/yl79o0aJFOuecc/Szn/1MVVVVyszM1JVXXqn//M//1HPPPafvfOc7XVErkgw9L2Fq+vTpRjnQIhgMau7cuRoyZIjmzp2rYDDodknwCHrvojvwthyOxIABA5Sfny+/36/8/HwNGDDA7ZLgEZzLYKLlut6ulQfX9QAAL3jggQeMcoDxNNAzHdEE+K5du/Twww/r/PPP14wZM/S73/1O4XBY/+f//B+tWLFCr732mv7t3/5NZ5xxRlfXiyRCz0uYSklJ0axZs2yz2bNnKyUlxeGK4GUTJkzQ008/zaol6BR676KrlJaWqr6+XtKXy+yFQiGXK4IXXXPNNXrxxRfj9uMF7HAug4mW6/fDH96yLIvregCAJ2RkZOiUU06xzU477TRlZGQ4XBG8hvE00DN1anaovLxcv/3tb7Vp0ybFYjFZlqXjjjtOl156qS699FIdc8wx3VUnklRLz8tf//rXsiyLnpfoMocvrQ8ATqPHEzqqpqZGoVCodfLAsiyFQiFNmjRJw4cPd7k6AL1BSx/5HTt2tG6jjzw66qv7zVdt376da3sAgCfEWz2pf//+DlcCr3r//fdtt7/33nuMqQGX+KxOrLF42mmnyefzKRgMasqUKbr88ss1ZsyY7qwvaWzbtk2SNHLkSJcr6XnC4bCuu+467d27V4MGDdITTzzB8sPokEgkotzc3Lh5eXk5b4ED6FaxWEw//OEPbXs9ZWZmat26dTzpi3ZZlqXFixfrjTfeOOThrUAgoFGjRqmwsJC35wA4Yt++fcrLy1MsFpPf71coFGIpfSQUjUZ14YUX2j6AHAgE9PzzzysQCLhQGQAAHRMOh3XxxRfHzZ977jnuVaNd3KMGnNOZudYj+l/Xp08fVVZWqrKystPf6/P59Ic//OFIPhZJqqX37q9+9SvddNNNDCjQYWvXrk2Yz5w506FqAPRGlZWVtpPfklRXV6fKykp961vfcrgqeEl1dbWqqqrabI9Go6qqqlJ1dbWOO+44FyoD0NsMGDBA3/72t7Vx40Z9+9vfZvIbHVJeXh539a1oNKry8nJddNFFDlcFAEDHLVq0KGFeVFTkUDXwIu5RAz1Tp98AN/5An0/vvPOO8Z/jNbwBDnQ9nq4D4DbeAIepljfAt27desiy+YFAQGeffbbuuusu3gAH4AhW5sKR4A1wAIDX8QY4THGPGnBOt70BXlBQcGQVAUA3SElJ0axZs7Ry5co22ezZsxlYAOh2fr9fixcv1k9/+tM22ZIlS5j8RkI+n08FBQW6/vrrbbcz+Q3AKaWlpaqtrZUk1dbWKhQK8aYKEgoEApo/f77uvffeNtmCBQuY/AYA9HjBYFBjx47V5s2b22Tjx49n8hsJcY8a6JmYAAfgaUcffbTt9szMTIcrAYBDffVtXqA9w4cPV15enn7961/Lsiz5fD7l5eVp2LBhbpcGoJeoqalRKBRSywJxlmUpFApp0qRJGj58uMvVoacbNGiQ7fZ412oAAPQ0ffr0sd2enp7ucCXwqry8PD3zzDNqaGho3ZaRkaErrrjCxaqA3o3XkgB4VjQajduDp6ioKG4vOsDOsmXLNHnyZC1btsztUuAhsVhMhYWFtllhYSGT4Oiw/Pz81jcLgsGg8vLyXK4IXrRp0yZdddVV2rRpk9ulwEMsy1JJSUnc7Z3omoZeiLEQutKqVas0ZcoUrVq1yu1SAPQijY2N2rhxo222ceNGNTY2OlwRvOrwMXVxcbFLlQCQmAAH4GHl5eVxJ7mj0ajKy8sdrghetXv37taLnY0bN2r37t0uVwSvqKystO3/LUl1dXWqrKx0uCJ4VWNjo8LhsKQve9BxkwWdFQ6HVVxcrN27d6u4uLh1fwISqa6uVlVVVZtxdTQaVVVVlaqrq12qDF7AWAhdZd++fSotLVUsFlNpaan27dvndkkAeomlS5ca5UCLw1ckZYVSwF1MgAPwrNzc3Lg95QKBgHJzcx2uCF51yy23HPL7+fPnu1MIPGfcuHFxL2gyMzM1btw4hyuCV915552HLD3MTRZ0ll3/ZqAjsrOzlZOTY5uNGTNG2dnZDlcEL2EshK5y5513tq4YEIvFGAsBcMwdd9xhlAMt7rzzzkN+z7kMcFeneoCfd955Hf7aYDCogQMHasSIEZo6darOOuusThfX1Z599lndfvvtkr5c6nbatGltvmbLli166KGH9Oabb6qxsVHHHXecLr/8cs2YMSPuRBsAdwQCAZ188snauXNnm+zkk0/m/yw6ZMOGDdq7d+8h2/bs2aMNGzZo8uTJLlUFr/D7/Ro1apTtcmmjRo2S38+zhkhsy5Yt2rFjxyHbtm/fri1btmj06NEuVQUvoX8zTPh8Pg0ZMsQ2Gzx4sHw+n8MVwUv8fr+OOeYY27fAjznmGMZC6BDGQgDclJ6eLp/PZ9v2xefz0QccHcK5DOh5OnUlUlNT0+Fff/vb37R582atXr1a+fn5mj9/vg4ePNhdf4+EPvnkEy1btkwZGRlxv+YPf/iDrrrqKv3lL3/RpEmT9KMf/UjNzc1avnw5bwMCPVBjY6Pt5Lck7dy5k+VjkRB95GGqubm53V5hzc3NDlcEr6F3KkzRvxmmmpubVVFRYZtVVFRwLkO7wuFwu9dktGNAIoyFALjts88+iztmtixLn332mcMVwWs4lwE9U6cmwC+99NIO/5oyZYrOPvtsBYNBWZalF198UQsWLOiuv0e7LMvSokWLdNRRRyk/P9/2a+rr67VkyRL5/X499dRTKiws1O23367169fr7LPP1ksvvaQXXnjB4coBtIcePTBFH3mYeuihh4xygN6pMEX/ZpjiXAYTixYtMsoBxkLoSps2bdJVV12lTZs2uV0KPGTGjBlGOcC5DOiZOrUE+vLlyzv9AQ0NDXrqqad0//336+WXX9arr76q7373u53+c0w89dRT+p//+R+tWbNG//M//2P7NRUVFfr888916aWXauTIka3b09PTdfPNN+uaa67RM888owsuuMCpsgEkcMcdd+iiiy5qNwfak5ubqwcffNB2Epw+8uiIOXPm6Pnnn283B9rT0jvV7mKZ3qnoiJb+zVu3bj3kzQK/36/Ro0fTvxkJcS6DieXLl+viiy9uNwfaw1gIXSUcDqu4uFh79+5VcXFx64tZQCJr1qyJ+9JcSw60h3MZ0DN1ezOmPn366Mc//rGuv/56WZal5557rrs/8hB/+9vfdN999+nqq6/W2LFj437dn//8Z0nSd77znTbZ2LFj1adPH73xxhtqamrqtloBdE56errOPfdc2+x73/sePXqQUCAQiNviYsGCBfSRR0KpqamaNm2abZafn6/U1FSHK4LX+P1+LV682DZrWZ0IaI/P51NBQUGbZRsty1JBQQH9m5EQ5zKYCAaDce+1jB8/nsknJMRYCF2ltLRUtbW1kqTa2lqFQiGXK4JXDBw4UH369LHNMjIyNHDgQIcrgtdwLgN6Jsf+5/3oRz+SJL355ptOfaQikYgWLlyoY489Vrfeemu7X/vBBx9Iko4//vg2WUpKir72ta8pEomwhCDQwzQ0NNhuP3DggMOVwKsGDRpku/3oo492uBJ4VU5Oju32UaNGOVsIPGvo0KG22wcPHuxwJUg29P9GR3Eug4m77rrLdvsvfvELhyuBV73//vu229977z2HK4FX1dTUKBQKtY59LMtSKBRSTU2Ny5XBK+Jdkw0ZMsThSpBs6P8NuKdTS6CbOOaYY5SRkdH6JJ4TfvWrX+mdd97RM888k/Cp4/r6eklS//79bfN+/fpJUtxeDh1hWZYOHjx4xN+fzCorK/XYY4/phhtuYEkQdFg4HNbmzZtts82bN+uzzz7jjQO0KxaLxb1hd9ddd2n16tU8pYl2sQ/BlGVZeuCBB+T3+9ssX/3AAw/o3/7t33iDF+1q2YfssA+hIziXoSssWbJEy5YtO+T33P9AR0QiEa1cudI2W7lypaZOnaqUFMduX8KDWsZChz/4F4vFGAuhQw4cOKAPP/zQNvvwww+1Z88e9e3b19mi4CmMpwHnWJbV4fO6oyPI1NTUuG9rdrW33npLjzzyiK699lqdffbZXfbnmgyYmpub9c4773RZLcmiqalJJSUlqqurU0lJiW677TalpaW5XRY84OGHH243v/322/WTn/zEoWrgRe+88472799vm+3fv1/PPvusTj/9dIergpewD8HU7t27tXXr1jbbY7GYtm7dqo0bN/LWAdoVbx+yLIt9CB3CuQxd4fB2cU1NTdz/QIds2LCh3fxXv/qVJk+e7FA18CLG0zD1H//xH+3mBQUF+td//VeHqoEXMZ4GnNXR+UPHJsDr6+tVV1cXd6nZrtSy9PkJJ5ygW265pUPf0/KGd7wDVaI3xDsiNTVVX//614/4+5PV2rVrW//d9+/frx07drQumQ+05+6771Z+fn67OW+Aoz2nnnqqfve739ke+/v3768f/OAHPKGJdrEPwdRpp52mV155RW+++WabN8DPOussnXvuubyxgnaxD8EU5zJ0hVmzZh3y+yeeeEKPP/64S9XAS77xjW/o5ZdfjpvfdNNNvAGOdjEWgqmSkhJNnz693Zw3wNEextOAc/761792+GsdG0G++OKLsizLkSddDh482LpsyciRI22/ZsmSJVqyZImuvvpq/exnP9OJJ56o7du368MPP9QZZ5xxyNdGIhF9/PHHSklJUXZ29hHX5fP5lJGRccTfn4xqamr029/+9pAePb/97W81depUDR8+3OXq0NNlZGQoKyvLtrVCVlaWBg4c6EJV8Jof//jHuvfee9tsnzNnTuvDUUB72Idgat68ebr++usP2eb3+3XzzTdzowUdwj4EU5zLYGLDhg1trsn27t2r119/nTd30SGzZs2yXQZ99uzZyszMdKEieA1jIZhIdL9+8ODBDlUCLzv77LO1ceNG2+2Mp4Gu05mH2hx57GTr1q2655575PP59P3vf7/bPy8tLU1XXHGF7a9vfvObkqScnBxdccUVrcujf+tb35Ikvfbaa23+vM2bN6uhoUFnn302S3N3IcuyVFJSEnf74b17gMM1NTXZTn5LUm1tbZtl+IDDWZYV922DP/7xjxyHkBD7ELrC8OHDlZeX1zqI9/l8ysvL07Bhw1yuDF7BPgQTnMtgIhqNqqioyDYrKipSNBp1uCJ4UV5envr06XPItoyMDF1xxRUuVQSvYSwEE/v27TPKgebmZtvJb0nauHGjmpubHa4IgNTJN8CfffbZDn9tOBzWrl27VFVVpb/85S+KxWI68cQTdckll3S2xk4LBoO66667bLPi4mK9/fbbuvTSSzVt2rTW7VOmTNG9996rF154QVdddVXrm+ONjY26//77JUlXXnllt9fem1RXV6uqqqrN9mg0qqqqKlVXV+u4445zoTJ4xT333JMw/9nPfuZQNfCieMchSRyH0CHsQ+gq+fn52rBhg/bu3ausrCzl5eW5XRI8Jj8/X7///e+1f/9+9evXj30IHca5DCbKy8vjTnJHo1GVl5froosucrgqeFFJSckhS+kXFxe7WA28iPE0jtTMmTMT5p2ZF0Hv89BDDyXM582b51A18LpNmzbpV7/6lW666SZNmDDB7XI8rVMT4D/96U+PqGeKZVnKzs7Wgw8+2GPfoO7Xr5+WLVumefPm6eqrr1Zubq4GDBig//7v/9YHH3yg888/X7m5uW6XmVSys7OVk5OjLVu2HPJWgc/nU05OjtFy8+gdFi5cqFdffbXdHGhPy3Fo69atbXqFjR49muMQEmrZh+wmDsaMGcM+hA4LBoOaO3du60VOMBh0uyQAvQTnMpjIzc3Vgw8+aDsJHggEuI+CDktPT2/390AijKdxpFavXn3Ii3J2OdCeOXPm6Pnnn283BzoiHA6ruLhYe/fuVXFxsc4++2zOZwY6vQS6ZVkd/uXz+XTqqafq1ltv1e9+9zudeOKJ3fF36DKTJk3SmjVrNGbMGG3YsEFPP/20UlNTtWjRIhUVFR3R5D/i8/l8KigoaLOknmVZKigo4N8bCaWlpWnq1Km22QUXXNBjH7hBz9FyHPrq5LckxWIxjkPokJZ9yA77EDprwoQJevrpp3nCF0ektLRU9fX1kqT6+nqFQiGXK4JXcC6DiUAgoPnz59tmCxYsUCAQcLgieNUtt9xyyO/j7VdAexhP40gMGDBARx11lG02cOBADRgwwNmC4DmpqalxH6LIz89XamqqwxXBq0pLS1tbvtbW1nJdb6hTb4D/8Y9/7PDXBoNBZWZm9rj/3HPnztXcuXPj5jk5OXrsscccrKh327Vrl+32Tz/9lD496JC//e1vttvff/99hyuBV3Ecgqny8nLb7S+88IJuuOEGh6sB0BvV1NQoFAq1PlhqWZZCoZAmTZqk4cOHu1wdvODJJ5+03f7EE09oyZIlDlcDr/n8889tt+/du9fhSuBVLctWf9WePXu0YcMGTZ482aWqAPQmdXV1ttu/+OILZwuBZx1//PG227/2ta85XAm8iuv6rtepN8CHDx/e4V9ZWVk9bvIbPUssFlNhYaFtVlhY2OaNTOBwBw8e1M6dO22znTt36uDBgw5XBK/hOARTzc3NKisrs83KysrU3NzscEXwslWrVmnKlClatWqV26XAQyzLUklJSdzth6+2BByusbFRGzdutM02btyoxsZGhyuCl0QiEa1cudI2W7lypSKRiMMVwWui0aiKiopss6Kiorg95gGgq3z66adx7//EYjF9+umnDlcEr+FcBlNc13ePTi+BDnSVysrKuE/X1dXVqbKy0uGK4DXz5s0zygGOQzD10EMPGeVAi3379qm0tFSxWEylpaXat2+f2yXBI6qrq1VVVdXmpko0GlVVVZWqq6tdqgxesXTpUqMcvdvatWuNcqC8vDzuxEA0Go272hIAdJWrr77aKAc4l8EU1/Xdw2d14tGBzZs3d8mHjh07tkv+HC/Ztm2bJGnkyJEuV9JzxGIx/fCHP7SdfMrMzNS6devk9/OMBuI7ePCgfvCDH8TNn332WWVkZDhXEDyH4xBMNTc364ILLoibv/DCC6yIgw6ZP3++duzY0fr7M844QytWrHCxIniFZVlavHixqqqqDtnu8/mUk5Oju+66ix7OaFdjY6MuuuiiuPnvf/97paenO1gRvCQSiSg3NzduXl5erpSUTnXfQy8TjUZ14YUX2k4cBAIBPf/88/SSB9CtPv3003YnuZ966ikdc8wxDlYEr+FcBlMt1/Vbt249ZEWKQCCgs88+m+v6r+jMXGunrkJmzJhh/I/s8/n09ttvG/0ZSA5+v1+zZ8/Wvffe2yb7yU9+wqQTEsrIyFBaWpqampraZGlpaUx+IyG/36/zzz/fdgnrKVOmcBxCQqmpqcrKylJtbW2bjHYw6KgtW7YcMvktSdu3b9eWLVs0evRol6qCV/h8Pk2bNq3NBLhlWZo2bRoXyUgoPT1dp5xyim1roVNPPZXJb7QrJSVFQ4cO1a5du9pkQ4cOZfIbCQUCAU2ePFkVFRVtsvPPP58JAwDdLtHkNpPfSCQQCOjkk0+2HU+ffPLJnMuQkM/nU0FBga6//nrb7VzXH5lO39m3LMv4FyB9uS+9/PLLttkf//hH9hUkdPDgQdvJb0lqamqiBzgSikaj+u1vf2ub/eY3v6FHDxJqaGiwnfyWpNraWjU0NDhcEbwmFoupsLDQNissLIzbiw5oYVmWysrK2lwQ+3w+rVu3jjE1EopGo/rb3/5mm/31r39lPIR2NTQ02E5+S9KuXbsYCyGhaDSqDRs22GYvvfQSxyAA3e6f//ynUQ40NjbaTn5L0s6dO9XY2OhwRfCi4cOHKy8vr/Xa3ufzKS8vT8OGDXO5Mu/q1AT4H//4x07/WrdunSZOnMgTCmijpa+BHfoaoCPoAQ5T9OiBqdtuu80oByorK23bMEhSXV2dKisrHa4IXtMypj58otuyLMbU6BDGQzDBWAimOAYBcNs111xjlANLly41yoEW+fn56tevnySpf//+ysvLc7kib+vUBPjw4cM7/GvgwIF69tlndd111+mVV16RZVk66aSTVFxc3F1/F3hMdna2cnJy2iwxHAgENGbMGGVnZ7tUGbzigQceMMqB3NzcuMsQBQKBdvsZApJ03333GeXAuHHjlJmZaZtlZmZq3LhxDlcEr2FMDVOMh2CCsRBMcQwC4LZVq1YZ5cAdd9xhlAN2WM3NXJc3N41Go1q7dq2+//3vq6SkRPX19Ro6dKiWLVum559/XpMmTerqj4RHxetfQF8DdFRGRoZOOeUU2+y0006jBzgSCgQCmj9/vm22YMECevQgoT59+mjkyJG22VlnnaU+ffo4XBG8xu/3a/HixbbZkiVL2kxqAodjTA1TjIdggrEQTHEMAuC2RMsLs/wwEklPT9e5555rm33ve99Tenq6wxXBq0pLS1VfXy9Jqq+vVygUcrkib+vSO2ovvPCCpk6dqmXLlmnv3r3KzMzUggULtGHDBl1xxRXcwEMb9DWAqXgDiNTUVIcrgVcNGjTIdvvRRx/tcCXwqmAwaLs9LS3N4UrgVaNHj9aIESMO2XbGGWdo1KhR7hQEz2FMDVPx2i1s2rTJ4UrgRfHGzQMGDHC4EnjV4eOgFqeddprDlQDoreLdR+T+Ijqqb9++ttt5GBAdVVNTo1Ao1Prmt2VZCoVCqqmpcbky7+qSGenXX39dl112mRYsWKCPPvpI6enpuv766/WHP/xB119/PTeA0a78/HxlZWVJkrKysuhrgA5raGjQtm3bbLNt27apoaHB4YrgNbFYTIWFhbZZYWGhYrGYwxXBa8LhsDZv3mybbd68WeFw2OGK4FV33nln68Oifr+fJdLQaYypcaQaGxu1ceNG22zjxo1qbGx0uCJ4CfsPTFmWpZKSEtuVTEpKSlj+E0C327Nnj5qbm22z5uZm7dmzx+GK4DVNTU2qqKiwzSoqKtTU1ORwRfCalvFQvO2Mh46M0QT4tm3bdM011+iGG27Q22+/Lb/fr2nTpumll17SggUL1L9//66qE0ksGAxq7ty5GjJkiObOnRv3TTrgcLfddptRDlRWVqqurs42q6uri/s2FNBi0aJFRjnQYsCAAcrPz5ff71d+fj5vzaHTgsGgJk+eLL/fr8mTJzOmRoctXbrUKEfvxv4DU9XV1aqqqmpzY9eyLFVVVam6utqlygD0FjNmzDDKgXvuuccoB1rGQ9Fo9JDt0WiU8ZABn3UEjw784x//0IoVK7Rhw4bWAer3v/99zZ8/XyeddFKXF5kMWt5SjdcbC0DnNTQ06JJLLombr1+/nmVm0K5YLKYf/vCHtpPgmZmZWrduHe070K5wOKyLL744bv7cc88xCQXAEeFwWNddd5327t2rQYMG6YknnuD4gw5pbGzURRddFDf//e9/T99CxMX+A1OWZWnx4sXasmXLIZPgPp9POTk5uuuuu9q8HQ4AXWnPnj2aPn163Hzt2rUaPHiwgxXBa5qamnThhRfGzZ9//nlWSUa7WsZDW7duPWRF0kAgoLPPPpvx0Fd0Zq61U3f19+zZozvuuEMXXHCBXnrpJVmWpbFjx2rdunUqLi5m8huAo/r06RP3QHfWWWcx+Y2E/H6/Fi9ebJstWbKEyW8kFAwGNXbsWNts/PjxTD4BcExpaalqa2slSbW1tQqFQi5XBK9IT0/Xueeea5t973vfY/IS7WL/gSmfz6eCggLbN8ALCgq42Qug2w0ePLjdHuBMfiORtLQ0TZ061Ta74IILmPxGQi3jIbuWMIyHjlyn7uxPnjxZ69atUyQS0WmnnaZHH31Ua9as0Zlnntld9QFAu+JNLjGwQEcNHTrUdjsXOOioQYMG2W4fOHCgw5UA6K1qamoUCoVaJw8sy1IoFFJNTY3LlcEr+vbta7udB0rREYynYWrHjh2227dv3+5wJQB6q/Z6gAMd8fHHH9tu/+ijjxyuBF41fPhw5eXltU52+3w+5eXladiwYS5X5l2dmgBvaGiQ9OU/fF1dnZYuXarzzjuvU78mTZrULX8ReNumTZt01VVXadOmTW6XAg8Jh8PavHmzbbZ582aFw2GHK4LXWJalkpIS26frSkpK2ryFAByuqalJFRUVtllFRYWampocrghetmrVKk2ZMkWrVq1yuxR4SMu5LN52zmVIhHMZTDQ3N6usrMw2KysrY+IACUWjURUVFdlmRUVFbXphAkBX+8c//mGUAw0NDa3LMh9u27ZtrfNqQCL5+fnKysqSJGVlZSkvL8/liryt02u7WpYly7L0z3/+UzU1NUf0C/iqcDis4uJi7d69W8XFxUxaosMWLVpklAPV1dWqqqqyXW6vqqpK1dXVLlUGr7jnnnuMcqDFvn37VFpaqlgsptLSUu3bt8/tkuARLeeywycIotEo5zJ0COcymHjooYeMcqC8vDzuJHc0GlV5ebnDFQHobW644QajHLjtttuMcqBFMBjU3LlzNWTIEM2dO5fWioZ8VideCbB7s+BIFBQUdMmf4yWdacze26xatUq//vWvZVmWfD6ffvSjH2nmzJlulwUPCIfDuvjii+Pmzz33HCcJtMuyLC1evFhbtmw5ZBLc5/MpJydHd911Fz1W0K6mpiZdeOGFcfPnn3+elgzokPnz5x+y/OcZZ5yhFStWuFgRvKLlXFZVVdUmGzNmDOcyJMS5DCaam5t1wQUXxM1feOGFuH1VAenLSe4LL7zQdhI8EAjo+eefVyAQcKEyAL3FP/7xj3YnuR977DEdf/zxDlYEr2loaNAll1wSN1+/fj2thYAu0pm51pTO/MG9ceIa3Stev8JJkyZp+PDhLleHni4YDCotLc12Wca0tDQmv5GQz+fTtGnT2kwaWJaladOmMWGAhNLS0nTKKado586dbbJTTz2VCQN0yJYtW9r0vty+fbu2bNmi0aNHu1QVvMLn82nixIm2E+ATJ07kXIaEOJfBRGpqqrKyslRbW9smGzRoEJPfSCgQCOiyyy6zXUr/iiuuYPIbQLdLNLnN5DcS6dOnj0444QR9+OGHbbKTTjqJyW/AJZ1eAh3oKvQrhKn6+vq4PQmbmppUX1/vcEXwGsuyVFZWZtsDfN26dRyHkFAkErGdMJCk9957T5FIxOGK4DWxWEyFhYW2WWFhoWKxmMMVwWtisZgeffRR2+yRRx5hH0JCnMtgoqGhwXbyW5L27t1Lz0skFIvF9NJLL9lmFRUVnMcAdLs9e/YY5UAsFtNnn31mm+3du5dzGeASJsDhGvoVwtTs2bONcoAe4DC1du1aoxyorKxUXV2dbVZXV6fKykqHK4LXsA/BFOcymKDnJUxxHgPgthkzZhjlAOcyoGdiAhyuyc7OVk5Ojvz+Q3fDQCCgMWPGKDs726XK4BXx3nbqaA60HIfscBxCR0yfPt0oB8aNG6fMzEzbLDMzU+PGjXO4IngN+xBMcS6Difvuu88oBziPAXDbmjVrjHKAcxnQMzEBDtf4fD4VFBTYLj1stx04XL9+/eL2is/Ozla/fv0crghe03K8scNxCB2RkpKiWbNm2WazZ89WSkqKwxXBa/x+vxYvXmybLVmypM2DgsDh2IdginMZTPTp00cjR460zc466yx6XiIhzmMA3DZ48GClpqbaZqmpqRo8eLDDFcFrOJcBPRP/8+Cq4cOHKy8vr3WSyefzKS8vT8OGDXO5MnhFOBy23X7w4EGHK4FXlZeX225/4YUXHK4EXvXaa6/Zbn/llVecLQRJhz5hMMU+hI56//33bbe/++67DlcCL3r77bdtt2/fvt3hSuBVVVVVtts3b97scCUAeqvm5uZObQcO9+qrr9puf/nllx2uBEALJsDhuvz8/NY3dfv166e8vDyXK4JX1NXVqba21jarra2N23sFaNHc3KyysjLbrKysjAsdJHTw4EHt3LnTNtu5cycP4yChWCymwsJC26ywsJAJTCTEPgRTjY2N2rhxo222ceNGNTY2OlwRvGTXrl2KRqO2WTQa1a5duxyuCF7DNRkAt73zzjtGOdDU1KSKigrbrKKiQk1NTQ5XBEBiAhyAh1177bVGOfDQQw8Z5cC8efOMcqCysjLuA1t1dXWqrKx0uCJ4DfsQTC1dutQoR+82Y8YMoxzgmgyA226++WajHLjnnnuMcgDdgwlwuK60tFT19fWSpPr6eoVCIZcrglc8+eSTRjkwZ84coxx44IEHjHJg3LhxyszMtM0yMzM1btw4hyuC17APwdQdd9xhlKN3W7NmjVEOcE0GwG3333+/UQ4sXLjQKAfQPZgAh6tqamoUCoVkWZYkybIshUIh1dTUuFwZvCDezd6O5kBqaqrGjh1rm40fP16pqakOVwSvycjIiLufpKamKiMjw+GK4DV+v1/nnHOObXbOOefI72e4jvb5/X4tXrzYNluyZAn7EBJKT0/X8OHDbbOvfe1rSk9Pd7gieMnQoUONciA1NVWnnHKKbXbKKadwTQag251++ulGOZCWltbueDotLc3higBITIDDRZZlqaSkJO72lklxIJ69e/ca5UA0GtWWLVtss7/85S9x+xkCLQ4cOBC3L2Fzc7MOHDjgcEXwmkgk0m6vsEgk4nBF8KLRo0drxIgRh2w744wzNGrUKHcKgqc0NzfHfQD5448/pv8u2vXZZ58Z5UBzc7N27txpm+3cuZNjEIBu9+abbxrlQFNTU7vjaXqAA+5gAhyuqa6uVlVVVZsJpmg0qqqqKlVXV7tUGbyCfnMwVV5eHneSOxqNqry83OGK4DU33nijUQ6sXbvWKAda3HnnnfL5fJIkn8/HstXoMPrvwgTXZDDFMQhdadOmTbrqqqu0adMmt0uBh7B8NUzRAxzomZgAh2uys7OVk5PTZlnGQCCgMWPGKDs726XK4BX0m4Op3NxcBQIB2ywQCCg3N9fhiuA1Dz74oFEOTJ8+3SgHWqSnpysYDEqSgsEgy1ajw+i/CxNck8EUxyB0lXA4rOLiYu3evVvFxcUKh8NulwSPYPISpniIAuiZmACHa3w+nwoKClrfVEm0HTjcoEGD4vZQSUtL06BBgxyuCF4TCAQ0f/5822zBggVxJ8eBFn379tUJJ5xgm5100knq27evswXBc1JSUjRr1izbbPbs2UpJSXG4InhVaWlp643ecDisUCjkckXwitTUVE2bNs02y8/Pp/8u2jVw4ED16dPHNsvIyNDAgQMdrghewzEIXaW0tFS1tbWSpNraWsZC6LCzzjrLKAfS0tI0depU2+yCCy6gBzjgEibA4arhw4crLy/vkOUa8/LyNGzYMJcrg1fEe7uJt57QUZ9//rntdnrIo6MGDx5suz0rK8vhSuBV3/72t223T5gwweFK4FU1NTUKhUKyLEuSZFmWQqFQ3D50wOFycnJst9NHHh3x9a9/3Xb7ySef7HAl8Kp4K2+df/75DlcCr2IsBFPxXsTiBS10VLz5jGOOOcbhSgC0YAIcrsvPz2+dJMjKylJeXp7LFcErvvjiC+3fv982279/v7744gtnC4LnRCIRrVy50jZbuXKlIpGIwxXBa8LhsDZv3mybbd68mWX3kJBlWSopKbHNSkpKWm/iAfHE24datrMPIZFYLKbCwkLbrLCwULFYzOGK4CUNDQ3atm2bbbZt2zY1NDQ4XBG8puV8dXh7PL/fz3kMHcJYCKY++eSTuPuJZVn65JNPHK4IXsP9RaBnYgIcrgsGg5o7d66GDBmiuXPntvYuBBK5+uqrjXJg7dq1RjmwaNEioxyorq5WVVWVbVZVVaXq6mqHK4LXtOxD0Wj0kO3RaJR9CB1SWVmpuro626yurk6VlZUOVwQvue2224xyoOU8dvjDNrFYjPMYOoSxEEzNnDnTKAe4vwj0TEyAo0eYMGGCnn76aZb6RKc89dRTRjkwffp0oxxYvny5UQ5kZ2fbLj3s8/k0ZswYZWdnu1AVvKRlHzr8zblAIMA+hA4ZN26cMjMzbbPMzEyNGzfO4YrgJffdd59RDsQbC0niPIYOYSwEU6tXrzbKAe4vAj0TE+AAPOuoo44yyoGUlBSNHDnSNjvzzDOVkpLicEXwmmAwGLfX96BBg1jVBAn5fD5NmzatzXbLsjRt2jR6ziEhn8+ngoKCNvtKvO3A4fx+v8455xzb7JxzzmkzoQB8VZ8+fdS3b1/brF+/furTp4/DFcFrfD6fTjrpJNvsxBNP5DyGhBgLwdSxxx5rlAMpKSk64YQTbLMTTjiB+4uAS7iSBeBZn332mVEONDc3x+1Z+NZbb6m5udnhiuA1TU1Nqq2ttc327t2rpqYmhyuC11iWpbKyMtsbduvWraNnITpk+PDhysvLa92PfD6f8vLyNGzYMJcrgxdEIhFVVFTYZhUVFfQsRLvC4bAOHDhgm9XX1yscDjtcEbwmEomorKzMNisrK+MYhA5hLAQT8VpSdTQHGhsb9eGHH9pmH374oRobG50tCIAkJsDRQ2zatElXXXWVNm3a5HYp8JAZM2YY5cBDDz1klAP33HOPUQ609Cw8fKLbsix6FqJT8vPzFQgEJH255GdeXp7LFcEr6FkIE4sWLTLKAY5B6Cr5+fnq16+fJKl///6MhdBhnMtgaunSpUY5gO7BBDhcFw6HVVxcrN27d6u4uJgnxNFha9asMcqBOXPmGOXAwoULjXIgXs9Cv99Pz0J0yp49e1rfkotEItqzZ4/LFcEr6FkIE8uXLzfKAY5B6A6sooTO4FwGU3fccYdRDqB7MAEO15WWlrYuH1tbW6tQKORyRfCKgQMHxu0pl5GRoYEDBzpcEbwmNTXVtveu9OXT46mpqQ5XBK9JS0vT1KlTbbMLLrhAaWlpDlcEr2npTWj3Bjg9C9EZBQUFh/x+7ty5LlUCr0lJSdGsWbNss9mzZ9OzEO0KBoMaO3asbTZ+/HgFg0GHK4LXcAxCVyktLVV9fb2kL1swcH8RHZWTk2OUA+np6Tr33HNts+9973tKT093uCIAEhPgcFlNTY1CoVDrTV/LshQKhVRTU+NyZfCKeD2a6buLjsrMzLTd3rdvX4crgVdVVlbabv+f//kfhytBsuHNFXRUKBRSQ0PDIdsOHjzIjV902FtvvWW7fevWrQ5XAi+Kt+LErl27HK4EXlVXV2e7/fPPP3e4EngV9xcBuO3w67EWBw4ccLgSAC2YAIdrLMtSSUlJ3O3c9EUiu3fvbl3q83CRSES7d+92uCJ4TSQS0cqVK22zlStXxt2/gBZ1dXWtq5gcrra2Nu7NPKBFy7jn8De9fT4f4yF0COcymAqHw9q8ebNttnnzZlpUoV0HDhzQhx9+aJt9+OGH3PRFQs3NzSorK7PNysrK4j70DrTg/iJMxXsQsKM5wHga6JmYAIdrqqurVVVVpWg0esj2aDSqqqoqVVdXu1QZvGLGjBlGObB27VqjHLj22muNcqBlPBSLxQ7ZHovFGA+hQziXwdSiRYuMcvRuN954o1EOPPTQQ0Y5wP1FmFqwYIFRDjCeRlfatGmTrrrqKm3atMntUjyPCXC4Jjs7Wzk5OfL7D90NA4GAxowZo+zsbJcqg1esWbPGKAemT59ulANPPvmkUQ60jIcO5/P5GA+hQziXwdTy5cuNcvRuDz74oFEOzJkzxygHuL8IU/fee69RDjCeRlcJh8MqLi7W7t27VVxczOoBhpgAh2t8Pp8KCgpsl/y02w4cbsiQIUpJSbHNUlJSNGTIEIcrgtekpKRo1qxZttns2bPj7l9Ai8zMTGVlZdlmgwcPjttjHmjRMu45nGVZjIfQIZzLYCoYDGrs2LG22fjx4xUMBh2uCF7St29fnXDCCbbZSSedpL59+zpbEDwnNTVV06ZNs83y8/OVmprqcEXwGu4vwtSZZ55plAOMp9FVSktLW1st1tbWKhQKuVyRtzEBDlcNHz5ceXl5rYNRn8+nvLw8DRs2zOXK4BXxLoa5SEZHvf/++7bb3333XYcrgVcdPHjQdnt9fb3DlcCrdu3aZbv9008/dbgSeNXRRx9tu52HcNBR8d4siHeOA76qoaHBdjv9v9FR8c5XPECBjuL+IgC3xbu/+N577zlcCbyqpqZGoVBIlmVJ+vLFiFAopJqaGpcr8y4mwOG6/Pz81qeggsGg8vLyXK4IXvHZZ5/FvdnS0NCgzz77zOGK4DWNjY3auHGjbbZx40Y1NjY6XBG8huMQTMViMRUWFtpmhYWFbXqDA4eLRqMqKiqyzYqKitr0wwQO19DQoG3bttlm27Zti3ueAyRp//79cR/k2rVrl/bv3+9wRfCaSCSilStX2mYrV65UJBJxuCJ4VX5+fuvqXFlZWdxfRIdt3brVKAf27dunL774wjb74osvtG/fPmcLgudYlqWSkpK421smxdE5TIDDdY2Nja1vHITDYSac0GEzZswwyoGlS5ca5QDHIZiqrKxUXV2dbVZXV6fKykqHK4LXlJeXx53kjkajKi8vd7gieM1tt91mlKN3i9eCoaM5sHbtWqMcaBEMBjV37lwNGTJEc+fOZclhdNjtt99ulAMzZ840yoHq6mpVVVW1ubaPRqOqqqpSdXW1S5V5GxPgcN2dd955yLIOTDiho9asWWOUA3fccYdRDnAcgqlx48bFXfYzMzNT48aNc7gieE1ubq4CgYBtFggElJub63BF8Jr77rvPKEfvFu/N3Y7mwPTp041y4KsmTJigp59+WhMmTHC7FHjI3XffbZQDq1evNsqB7Oxs5eTktLbyaOHz+TRmzBhlZ2e7VJm3MQEOV23ZskU7duw4ZNv27du1ZcsWlyqClwwcONAoB9LT01uXSDtcVlaW0tPTHa4IXsNxCKb8fr/OOecc2+ycc86R389wHe0LBAI6+eSTbbOTTz457uQ40KJPnz5x++z27dtXffr0cbgieEn//v2NciAlJUVTp061zXJzc5WSkuJwRQB6m7PPPtsoBwYMGGCUAz6fT9OmTWuz1LllWZo2bVqbiXF0DHfU4Bp6XsJUov4p9FdBIo2NjaqtrbXNamtracmAhOL1eOpoDkQiEVVUVNhmFRUV9L1EQo2Njdq5c6dttnPnTs5lSCgcDuvAgQO22YEDB1rbVQF2PvvsM6MciMVi+tOf/mSbvf7669wbAtDtNm7caJQD+/fvN8oBy7JUVlZm+wb4unXr6AF+hJgAh2voeQlT9FeBKXqAw9TVV19tlAP0vYQpzmUwtWjRIqMcvduMGTOMcoB7QwDctmzZMqMcmDVrllEOtPQAt3sDnB7gR44JcLiGnpcwRX8VmKIHOEw99dRTRjlA30uY4lwGU8uXLzfK0butWbPGKAe4NwTAbUuWLDHKgZUrVxrlQEsPcDv0AD9yTIDDNX6/X4sXL7bNlixZQs9LJDRgwAAdddRRttnAgQPpr4KE0tPTde6559pm3/ve9+gBjoSOOuqouL0t2ztGAS1SUlLiPg0+e/Zs+l4iIc5lMBUMBjV27FjbbPz48QoGgw5XBC8ZOHBg3D7xGRkZGjhwoMMVwWu4NwTAbfHG0h3Ngf79+2vo0KG22bHHHhv3vhHQwufzqaCgwDYrKCigB/gRYhQJV8U7MQwePNjhSuBV8frr0msOHcVxCKbq6+ttt8dbyhE43De+8Q3b7SeddJLDlcCr4r0dN2bMGIcrgVf985//tN3+8ccfO1wJvCgSidhub25udrgSeNUTTzxhu/3xxx93uBIAAI5MbW2t7fY9e/Y4XAm86vXXX7fd/tprrzlcSfJgAhyusSxLJSUlbZ5e8fl8KikpadPvADjchx9+aJQDzc3NKisrs83Kysq4aYeEPvnkk7jnK8uy9MknnzhcEbwmFoupsLDQNissLFQsFnO4InhNNBpVUVGRbVZUVKRoNOpwRfCa+vp61dTU2GY1NTVxH/QCpC9v6sYbMzc3N3PTFwkdPHhQO3futM127typgwcPOlwRvGzVqlWaMmWKVq1a5XYp8JDKykqjHNi9e3fcBwIjkYh2797tcEXwmkgkEnep/JUrV8bdv9A+JsDhmurqalVVVbWZOLAsS1VVVaqurnapMnjF7NmzjXLgoYceMsqBmTNnGuVAZWVl3NUC6urquNmChMrLy+NOckejUZWXlztcEbyGMTVMzJgxwygH5s2bZ5QDLfbt26fS0lLFYjGVlpZq3759bpcEj6AHOEwxHoKptWvXGuWwxwQ4XJOdna2cnBzbbMyYMcrOzna4InjNo48+apQDc+bMMcqB1atXG+XAuHHjlJmZaZtlZmbGXdoaaJGbm6tAIGCbBQIB5ebmOlwRvIYxNUysWbPGKAceeOABoxxoceedd7aunhSLxbR06VKXK4JXLFu2zCgHGA/B1PTp041y2GMCHK7x+XyaOHGibTZx4sQ2S6MDhzvhhBOMciA1NVUjR460zc4880ylpqY6XBG85thjjzXKAb/fr/PPP982mzJlivx+hutoXyAQ0OTJk22z888/P+7kONCiX79+7T5E0a9fP4crgpcMHjzYKAcyMjLUp0+fuFlGRobDFcGLtmzZoh07dhyybfv27dqyZYtLFcFLEj10zEPJSGTIkCFGOZCSkqJZs2bZZrNnz1ZKSorDFSUH7qjBNbFYLO7bBI888gg9L5FQov4p9FdBIpFIRNu2bbPN3nrrLfqrIKFE7Tpo54FEotGofvvb39pmv/nNb+jfjISi0ag2bNhgm7300kvsQ0jowIED7S6jf+DAAYcrgpdwTQZT4XBYDQ0NttnBgwcVDocdrgheE4vFVFhYaJsVFhZyfxEJJWoZREshJLJ3716jHJCkvLy8Ng8FZmRk6IorrnCpIu9jAhyuoeclTNFfBaborwJT8Z7O7GgO0L8ZptiHYOrGG280ytG7cU0GU4sWLTLKAe4vwtQvf/lLoxxgPISuUlJScsjvi4uLXaokOTABDtfQ8xKm6K8CU/RXgamVK1ca5QD9m2GKfQimHnzwQaMcvRvXZDC1fPlyoxzg/iJM3XLLLUY5wHgIXSU9Pb3d36NzmACHa/x+vxYvXmybLVmyhJ6XSGjIkCFx+1+kpKTQXwUJ0V8FprKzs41yIBAIaP78+bbZggUL6N+MhNiHYKpv37464YQTbLOTTjpJffv2dbYgeArXZDAVDAY1duxY22z8+PEKBoMOVwSv4f4iTCV6YJQHSpHIoEGDlJaWZpulpaVp0KBBDlcErzr8gZt41/roGEYAcFW8/hf0CUNHxevRTO9mdNRrr71mu/2VV15xthAAvdarr75qu/2///u/Ha4EXjVixAjb7aeddprDlcCrvvjiC9vtn332mbOFwJPaa8MAdMS+fftst3/++ecOV4JkQ/9vAE6Jd7zhOISO2rBhQ5v5sj179mjDhg0uVeR9TIDDNdFoVEVFRbZZUVERF8tI6J133jHKgYMHD2rnzp222c6dO3Xw4EGHK4LXfPDBB0Y5EA6HtXnzZtts8+bNCofDDlcEr7EsSyUlJfL5fIds9/l8KikpkWVZLlUGr9i3b1/cCfAvvvgi7sQUIEmffPJJ3OOMZVn65JNPHK4IXsM1GUzFYjEVFhbaZoWFhUw+IaG1a9ca5cDu3bvbfUmLl/2QCHNl3YMJcLimvLy83SfFy8vLHa4IXnPzzTcb5cC8efOMcuDHP/6xUQ4sWrTIKAeqq6tVVVXVZgLKsixVVVWpurrapcrgFTNnzjTK0bux/8AU12QwVVlZqbq6Otusrq5OlZWVDlcEr1m9erVRDsyYMcMoB5gr6x5MgMM1ubm5cXsSBgIB+qsgofvvv98oBx544AGjHHjkkUeMcmD58uVGOZCdna2cnBzbbMyYMcrOzna4IngNN31hgv0Hprgmg6lx48YpMzPTNsvMzNS4ceMcrghew8NcMLVmzRqjHGCurHswAQ7XBAIBXXbZZbbZ5ZdfHvc/PNDi9NNPN8qBjIwMpaam2mapqanKyMhwuCJ4zYknnmiUA8FgUFlZWbbZoEGDFAwGHa4IXuPz+TRkyBDbbPDgwW2WRgcON2DAAKMcvduxxx5rlAMZGRlxz1U+n49rMiTk9/s1bdo02+yHP/yh/H5uf6N906dPN8qBeNdjHc2BQCCga665xjabNWsWc2VHiBEAXBOLxfTSSy/ZZi+++CI9epBQVVWVUQ4cOHBAzc3Ntllzc7MOHDjgcEXwmg8//NAoB5qamlRbW2ub7d27V01NTQ5XBK9pbm5WRUWFbVZRURH3PAe02LVrl1GO3u0f//iHUQ7s27ev3T7y+/btc7gieE0sFlNZWZlttm7dOu4vIqGHHnrIKAf++c9/GuWAZVl64403bDO7lmfoGCbA4Rp69MAUfVNh6sYbbzTKgdmzZxvlwD333GOUA9ywgyl6FsLEDTfcYJQDLD0MU9xfhKnf/e53RjkQ783djuZAdXV13Jf5qqqqVF1d7XBFyYEJcLiGHj0wRd9UmHrwwQeNcuDRRx81yoGFCxca5cCcOXOMcoCehTDx2GOPGeUAfeRhivuLMHXppZca5cCqVauMciA7O1s5OTlt2nYEAgGNGTNG2dnZLlXmbUyAwzV+v1+LFy+2zZYsWUKPHiSUk5NjlAN9+/bVCSecYJuddNJJ6tu3r7MFwXPi7T8dzYG0tDRNnTrVNrvggguUlpbmcEXwmtTU1Lh9L/Pz85WamupwRfCaoUOHxu0pFwgENHToUIcrgpccf/zxRjkwYMAAHXXUUbbZwIEDNWDAAGcLgudwfxGmeKAUpoYNG2aUAz6fTwUFBfL5fB3ajo5hBABXjR49WiNGjDhk2xlnnKFRo0a5UxCAXmf//v222+k1B8Ap8S6GjznmGIcrQbKJRqNulwCPiDc5wKQBACc0Nzfbbm9sbHS4EngV9xcBuC3eg8c8kIyOGj58uPLy8lonu30+n/Ly8niAwgBXs3DdnXfe2Xpjxe/364477nC5InhFoj5O9HlCInV1daqtrbXNamtr4/YRA1p88MEHRjkQiUS0cuVK22zlypWKRCIOVwSvaW5uVllZmW1WVlYWd1IBaLFnz564+0lzc7P27NnjcEXwkvfff98oBz7//HMdOHDANjtw4IA+//xzhyuCV3F/EUdq3rx5RjnAeBpdJT8/X1lZWZKkrKws5eXluVyRtzEBDtcNGDBA3/72tyVJ3/72t1neCh22ZMkSoxy49tprjXLgxz/+sVEOrF271igHHnroIaMcmDFjhlGO3u2mm24yygGOQegqAwYMUH5+vvx+v/Lz87m/iA579913jXKAcxm6SjAY1OTJk+X3+zV58mQFg0G3S/I0JsDhunA4rLfffluS9PbbbyscDrtcEbxi2bJlRjnw5JNPGuXAI488YpQD06dPN8oBehbC1Jo1a4xy9G6/+tWvjHKAYxC60jXXXKMXX3xR11xzjdulwENOO+00oxzgXIauEg6HtWHDBsViMW3YsIG5MkNMgMN1paWlrUsQ19bWKhQKuVwRvGLcuHFGOZCZmdm6rMzhBg8erMzMTIcrgteceOKJRjmQkpKiWbNm2WazZ89WSkqKwxXBa1JTUzVt2jTbLD8/n55zSGjw4MHt9iwcPHiwwxXBS77xjW8Y5cDRRx+tvn372mb9+vXT0Ucf7XBFAHqbBx54wCgHGE+jqzBX1rWS9o7a559/rj/84Q965ZVXtHPnTu3atUupqak65ZRTdNlll+nyyy9v7QvzVVu2bNFDDz2kN998U42NjTruuON0+eWXa8aMGQoEAi78TZJbTU2NQqGQLMuSJFmWpVAopEmTJmn48OEuVwegN4j3JN3BgwcdrgRAb/XPf/7Tdnt1dbXDlSDZRKNRt0uAR7TXsxAAulu8Y01TU5PDlQAAcGQYT8MUc2VdL2nfAH/xxRe1ZMkSvfnmmzrzzDM1c+ZMTZ48We+//76WLFmim2++uXVHavGHP/xBV111lf7yl79o0qRJ+tGPfqTm5mYtX75c8+fPd+lvkrwsy1JJSUnc7Yf/fIDDvfLKK0Y58Pnnn+vAgQO22YEDB/T55587XBG8Ztu2bUY50NTUpIqKCtusoqKCG79IqLm5WWVlZbZZWVkZN1yQUE1NjVGO3m3Hjh1GObB37964452mpibt3bvX4YrgZatWrdKUKVO0atUqt0uBh9BiEaY++ugjoxxgrqx7+Kwk/ZfbtGmTDhw4oIkTJx7y5vaePXs0bdo0ffLJJ7r//vs1ZcoUSVJ9fb0mTZqk+vp6/frXv9bIkSMlSY2NjZo5c6a2bt2qFStW6IILLjiielpugLf8ufjywH/99dfHzR9//HEdd9xxDlYEr5k8eXLCr9mwYYMDlcCrLrzwwnYnl9LS0vT88887WBG8huMQTN1111169dVX4+bf/e539bOf/czBiuA1DzzwQLvnqgsvvFDz5s1zsCJ4DecymGD/gampU6e2u2JJIBCI+7Ag8FX79u1TXl6eYrGY/H6/QqGQBgwY4HZZ8ADOZTDFPgRTzJV1XGfmWpP2DfAJEyZo0qRJbZYtHzx4sPLz8yVJlZWVrdsrKir0+eef68ILLzzkHy49PV0333yzJOmZZ55xoPLeIzs7Wzk5OW2Wog8EAhozZoyys7NdqgxesXjxYqMcWLNmjVEO3HfffUY5sHDhQqMcmDNnjlEOPPnkk0Y5ereioiKjHOCaDF3lzjvvVCwWkyTFYjEtXbrU5YrgFeeee65RDjz++ONGOdAyV+bz+Q7Z7vP5mCszkLQT4O1JTU2VJKWk/P8t0P/85z9Lkr7zne+0+fqxY8eqT58+euONN1iGsgv5fD4VFBTY/qe22w4c7nvf+55RDhx99NFGOZDoaUNWfkEiaWlpSk9Pt82CwaDS0tIcrghek5qaqqFDh9pmxxxzTOu1DxBPon5y9JtDe0aMGGGUA4MGDTLKAUnasmVLm5YL27dv15YtW1yqCF6yZMkSoxxI9GYub+4iEZ/Pp2nTprVZ6tyyLE2bNo25siOUkvhLkkskEtGzzz4r6dDJ7g8++ECSdPzxx7f5npSUFH3ta1/T+++/r+rqap188slH9NmWZengwYNH9L3J6uijj9Zll12m//zP/5RlWfL5fLrssst01FFH8W+FhP70pz+1m//Xf/2XzjnnHIeqgRcl6vFdU1PDJDjatX379nbzyspKnXHGGQ5VAy/av3+/GhsbbbNwOKxdu3apf//+DlcFL2nZT+x8+umn+uyzzxQMBh2uCl7y4Ycftpu//fbbOuGEExypBd6TqMf35s2bmQRHu/bs2dNu/o9//EODBw92qBp4USwW01133WWb3XXXXVq9enWb1SeBr/rBD37Qbj558uTW+QTAzt///vd28+3bt+ukk05yqBp4kWVZCoVCtllpaalOOeUUJsH/V8s8Ykf0ugnw++67Tzt37tS55557yAR4fX29JMW9wdivXz9JUl1d3RF/dnNzs955550j/v5kdcYZZ+g3v/mNLMuS3+/XiBEj+HdCh9xzzz0J84EDBzpUDbzo5z//ebv5DTfcoF/84hcOVQMv6siT4suXL3eoGnjRsmXL2s1/8pOf8MYB2vXwww+3m99+++36yU9+4lA18KJFixa1m99yyy2cyxDXz372s4Q5+w/ak+gYdMMNN7APoV3vvPOO9u/fb5vt379fzz77rE4//XSHq0Ky4V412pPoXHbrrbdyLkO7du/era1bt9pmW7du1caNGzVkyBCHq+q5OrpaYq+aAF+1apWeeOIJnXjiibr77ruP6M8wecoiNTVVX//614/4+5PVxx9/3NqjJxqNKisrS1/72tdcrgpesHDhwnYnwRcuXMhFDtr12GOP6dprr2035w1wtGfZsmXtTk4uW7aM4xDa9fDDD2vGjBnt5rwBjvbcfffdys/PbzfnDXC055e//KVuueWWdnPeAEc8d911V7uT4HfddRdjIbTrscce0w033NBuzhvgaM+pp56q3/3ud7aT4P3799cPfvAD3gCHMc5laM+KFSt06623tpvzBjjac9ppp+mVV17Rm2++2TpXJkl+v19nnXWWzj33XN4A/19//etfO/y1vWYCfPXq1Vq+fLlOPvlkrV69us1boS1veMd7YjDRG+Id4fP5lJGRccTfn6wWLlx4yO//9V//lWVl0CHf//73250A//73v+9gNfCijIwM9e3bVwcOHGiT9evXj56XSGjcuHFGOZCRkaGhQ4faLmF97LHHxu3tDLTIyMjQ2LFjtXnz5jbZ+PHjWQ0HCX3zm980ytG7jR071igHjj/+eKWmpqq5ublNlpqaatuqEDjcz372M/30pz9ts/3nP/956z1fIJ4NGzZo8uTJ7eZAexK1vqM1Hjpi3rx5uu666w7ZZlmWbr75ZvXt29elqnqezjwI0Csef1u5cqUKCwt1yimnaM2aNbZPjp544omS7PufRSIRffzxx0pJSVF2dnZ3l9urhEIhNTQ0HLLt4MGDcfsdAEBXi/ckOE/VAXDK6NGjbbePGjXK2ULgWWeeeabtdm60AAC8IN5KJaxggo7au3ev7fbdu3c7XAkAAF3Lsiy3S/CspJ8Af+SRR/Qf//EfOv3007V69WplZWXZft23vvUtSdJrr73WJtu8ebMaGhp09tlnd3hteSQWiUS0cuVK22zlypWKRCIOVwSvee6554xy4Isvvmi3V9gXX3zhbEHwnD/96U9GOdDU1KSKigrbrKKiQk1NTQ5XBK9hTA1Tf//7341y9G6bNm0yygGuyWAqGo2qqKjINisqKlI0GnW4InjNFVdcYZQDVVVVRjlgWZZKSkravJDl8/lUUlLCJPgRSuoJ8F/96ldasWKFRowYoVWrVrW7/N+UKVN09NFH64UXXtC2bdtatzc2Nur++++XJF155ZXdXnNvsnbtWqMcKCkpMcqBq6++2igHli5dapQD7bXy6EgOMKaGqZ/85CdGOXq3O+64wygHuCaDqfLy8riT3NFoVOXl5Q5XBK+pq6szyoFFixYZ5UB1dbWqqqoO6f8tSbFYTFVVVaqurnapMm9L2gnw3/3ud3rggQcUCAQ0ZswYrVmzRsXFxYf8+u1vf9v69f369dOyZcsUi8V09dVX62c/+5n+4z/+Q5dccom2bt2q888/X7m5uS7+jZLP9OnTjXKgoKDAKAeeeuopoxzgpi9MLVy40CgHGFPD1MMPP2yUo3fjYUCY4poMpnJzcxUIBGyzQCDA/VwklJmZaZQDy5cvN8qB7Oxs5eTk2GZjxoyhNfMRSnG7gO7y8ccfS/rySb/Vq1fbfs24ceN02WWXtf5+0qRJWrNmjR5++GFt2LBBjY2NOv7447Vo0SLNmDGDfrBdLCUlRSNHjjzkjfsWI0eOVEpK0u6e6CIXX3xxu295X3zxxQ5WAy866qijjHLgnHPOMcqBtLQ0HXXUUbbLex511FG030FCKSkpGj58uGpqatpkw4cPZ0yNhE466SSjHL3bhAkTjHKAazKYCgQCuuyyy1RWVtYmu/zyy+NOjgMt/vM//1OTJ09uNwfaE2/isqM54PP5NHHiRNvl8idOnMjc5BFK2jfA586dq/fee6/dX2vWrGnzfTk5OXrssce0efNmvfXWW/r973+va665hsFSN2hubrad/Jakbdu2qbm52eGK4DUPPPCAUQ589tlnRjnw8ssvG+VAQ0ND3N6WX3zxhRoaGpwtCJ4TDodtJ78lqaamRuFw2OGK4DXvvvuuUY7eraKiwigHdu3aZZQDsVhML730km324osvtllOFjhce5PfHcmBt956yygHYrGYHn30UdvskUce4Vx2hJJ2Ahw930MPPWSUA88//7xRDsyYMcMoB1jmCqZuu+02oxyg3xxMzZs3zyhH71ZUVGSUA1yTwVRlZWXcHs11dXWqrKx0uCIAvc2CBQuMcoBzWfdgAhyumTNnjlEOXHjhhUY5YLcSSGdygIknmLrvvvuMcoAHcWCKVZVgYv78+UY5wDUZTI0bNy5uj+bMzEyNGzfO4YoA9Db33nuvUQ5wLuseTIDDNampqZo2bZptlp+fr9TUVIcrgtfwtgpMDRw4UH369LHNMjIyNHDgQIcrgtdMnDjRKAf69OmjkSNH2mZnnXVW3GMU0CIYDGrs2LG22fjx4xUMBh2uCF5z2mmnGeXo3aZOnWqUA0OHDo3bdjAQCGjo0KEOVwSv8fv9Wrx4sW22ZMkS+f3c/kb7NmzYYJQDZ555plEOcC7rHvyrwVX19fW22/ft2+dwJQB6q3i9Uem7C8Apn332me32vXv3OlwJvGrPnj222+mbCgDwgmg02qntwOHef/992+3vvfeew5UAAHBk4t0D2r17t8OVJA8mwOGapqYmVVRU2GYVFRVqampyuCJ4zYoVK4xy4JNPPpFlWbaZZVn65JNPHK4IXvPSSy8Z5UB9fb1qampss5qamrgPCwItDhw4oA8//NA2+/DDD3XgwAFnC4LnVFVVGeXo3Z577jmjHNi5c6dRDkQiEa1cudI2W7lypSKRiMMVwWsmT55slAMvv/yyUQ5Eo1EVFRXZZkVFRTwUeISYAIdr7rnnHqMcePHFF41yYObMmUY5QP9mmJo9e7ZRDtx4441GObBo0SKjHL1bSUmJUQ4UFBQY5cDatWuNcgAwtXz5cqMcKC8vb3dFnPLycocrSg5MgMM1CxcuNMqBKVOmGOXA6tWrjXLgtttuM8qBRx991CgHHnzwQaMc4IYdTDB5CVM8RAFT06dPN8oBwBQPlMJUbm6uAoGAbRYIBJSbm+twRcmBCXC4Ji0tTSeccIJtduKJJyotLc3ZguA5t956q1EOHHvssUY5cP755xvlQL9+/YxyoG/fvvL77S/r/H6/+vbt63BF8JqcnByjHL3bxRdfbJQDp5xyilEOpKSk6Nxzz7XNvvvd7yolJcXhiuA1GzZsMMqBiRMnGuVAIBDQ6NGjbbOcnJy4k+NoHxPgcE1zc3PcfoUffPCBmpubnS0InjNv3jyjHPjrX/9qlAOPP/64UQ7s27fPKAf279+vWCxmm8ViMe3fv9/hiuA17733nlGO3u2JJ54wyoF3333XKAei0aj+9Kc/2Wavv/46fVORED3AYSrR8tQsX41EmpubtXnzZtussrKSubIjxAQ4XPPQQw8Z5QAXyjBF31SYWrdunVEOzJw50ygHZs2aZZQDc+fONcrRu5WWlhrlAA+2wxR9UwG47Ze//KVRDjBX1j2YAIdr5syZY5QDp512mlEO0DcVpn74wx8a5cDq1auNcmDlypVGOVBcXGyUo3fLz883yoEHHnjAKAfomwrAbbfccotRDjBX1j2YAIdrUlNTNW3aNNssPz9fqampDlcEr+FCGaa+/vWvG+XA9ddfb5QDAwYM0FFHHWWbDRw4UAMGDHC2IHhO//79NXToUNvs2GOPVf/+/R2uCF5z6qmnGuXo3a677jqjHODBdpgKBAKaP3++bbZgwQL6piIheoDDVKIHbXgQB4kwV9Y9mACHqzIzM2239+3b1+FKAAAA3BGvRzP9v9FRdXV1ttu/+OILZwsBAABwwY4dO2y3v/XWWw5XAgDAkTn++ONtt3/ta19zuJLkwQQ4XBOJROIuybhy5UpFIhGHK4LXTJ8+3SgH/v73vxvlwKJFi4xyYNeuXe32LNy1a5fDFcFrPvvsMzU0NNhmDQ0N+uyzzxyuCF6zceNGoxy9G2MhmOKaDKaamppUUVFhm1VUVKipqcnhiuA1kydPNsoB2lLBVDQaVVFRkW1WVFQU974R2scEOFyzdu1aoxzYs2ePUQ785Cc/McqBqqoqoxyYMWOGUQ6wD8HUsmXLjHL0boyFYIprMpi65557jHIAMBUKhYxyoLy8vN2XI8rLyx2uKDkwAQ7X8PYuTA0ePNgoBx5++GGjHMjJyTHKgTVr1hjlAPsQTC1ZssQoR+/GWAimuCaDqYULFxrlAGAqLy/PKAdyc3MVCARss0AgQB/5I8QEOFyTkpKiWbNm2WazZ89WSkqKwxXBa1hFAKZOOukkoxxYvny5UQ4MHTq03YucoUOHOlwRvGbgwIHq06ePbZaRkaGBAwc6XBG85txzzzXK0bsxFoIprslgKi0tTVOnTrXNLrjgAqWlpTlcEbxmw4YNRjkQb46jozkQCAQ0f/5822zBggVx7xuhfUyAw1Wvvfaa7fZXXnnF2UIAAABc4vP5OrUdOFwkErHd3tzc7HAlAAAAzjtw4IDt9v379ztcCQAAR+bVV1+13f7f//3fDleSPJgAh2sOHjyonTt32mY7d+7UwYMHHa4IXjN58mSjHNi+fbtRDsyZM8coB3bv3h138jISiWj37t0OVwSv2bNnT9yJ7ubmZu3Zs8fhiuA1L774olGO3m3mzJlGObBjxw6jHGhsbNTGjRtts40bN6qxsdHhiuA13F+Eqfvuu88oB8LhsDZv3mybbd68WeFw2OG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|
||
"text/plain": [
|
||
"<Figure size 2000x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 2000x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"for eco_name in ECO_NAMES:\n",
|
||
" # 加载数据\n",
|
||
" issue_df, link_df = load_df(eco_name)\n",
|
||
"\n",
|
||
" # 添加链接范围信息\n",
|
||
" link_df = add_link_scope(link_df)\n",
|
||
"\n",
|
||
" # 链接类型基本信息\n",
|
||
" link_types_overview = pd.concat(\n",
|
||
" [link_types_overview, get_overview(eco_name, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # CTI、LTI信息\n",
|
||
" time_interval = pd.concat(\n",
|
||
" [time_interval, get_time_interval(eco_name, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 评论信息\n",
|
||
" comment_scale = pd.concat(\n",
|
||
" [comment_scale, get_comment_scale(eco_name, issue_df, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 解决比例\n",
|
||
" solved_proportion = pd.concat(\n",
|
||
" [solved_proportion, get_solved_proportion(eco_name, issue_df, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 解决时长\n",
|
||
" sti = pd.concat(\n",
|
||
" [sti, get_sti(eco_name, issue_df, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
"\n",
|
||
"link_types_overview.to_csv(\n",
|
||
" RQ2_RESULT_DIR / \"link_types_overview.csv\", sep=\",\", index=False\n",
|
||
")\n",
|
||
"time_interval.to_csv(RQ2_RESULT_DIR / \"time_interval.csv\", sep=\",\", index=False)\n",
|
||
"comment_scale.to_csv(RQ2_RESULT_DIR / \"comment_scale.csv\", sep=\",\", index=False)\n",
|
||
"solved_proportion.to_csv(RQ2_RESULT_DIR / \"solved_proportion.csv\", sep=\",\", index=False)\n",
|
||
"sti.to_csv(RQ2_RESULT_DIR / \"sti.csv\", sep=\",\", index=False)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "grad_pro_env",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|