完成社区化分析的部分

grank/core.py

@@ -8,7 +8,7 @@
 
 from .libs import helpers
 from .libs import query
-from .script import activity,crawler
+from .script import activity,crawler,social
 
 @click.group()
 def main():
@@ -60,6 +60,7 @@ def repo(organization,repo):
     config = helpers.get_config()
     data = crawler.fetch_repo_data(organization,repo,config)
     activity.analyse_repo(organization,repo,data,config)
+    social.analyse_repo(organization,repo,data,config)
     pass
 
 @main.command()
@@ -79,6 +80,5 @@ def clean():
     helpers.clean_directory()
     pass
 
-
 if __name__ == '__main__':
     main()

grank/libs/helpers.py

@@ -157,19 +157,34 @@ def export_csv(series,name):
     """导出 Csv 文件"""
     series.to_csv("output/%s.csv" % name);
 
-def get_activity_avarage_instance():
+def get_activity_average_instance():
     """获取平均值 DF 实例"""
     if not os.path.isfile("output/activity_average.pkl"):
         pd.DataFrame(data={'name': [], 'score': []}).to_pickle("output/activity_average.pkl")
     return pd.read_pickle("output/activity_average.pkl")
 
-def set_activity_avarage(instance,owner,repository,score):
+def get_social_average_instance():
+    """获取活跃度平均值 DF 实例"""
+    if not os.path.isfile("output/social_average.pkl"):
+        pd.DataFrame(data={'name': [], 'score': []}).to_pickle("output/social_average.pkl")
+    return pd.read_pickle("output/social_average.pkl")
+
+def set_activity_average(instance,owner,repository,score):
     """保存中间值，并更新 csv 文件"""
     instance = instance.append(pd.Series({"owner":owner,"name":repository,"score":score}),ignore_index=True)
     instance = instance.drop_duplicates(subset=["owner","name"]).sort_values(["score"],ascending=False)
     instance.to_pickle("output/activity_average.pkl")
     instance.to_csv("result/activity_rank.csv")
 
+def set_social_average(instance,owner,repository,score):
+    """保存中间值，并更新 csv 文件"""
+
+    instance = instance.append(pd.Series({"owner":owner,"name":repository,"score":score}),ignore_index=True)
+    instance = instance.drop_duplicates(subset=["owner","name"]).sort_values(["score"],ascending=False)
+
+    instance.to_pickle("output/social_average.pkl")
+    instance.to_csv("result/social_rank.csv")
+
 def series_to_pickle(df,name):
     """将数据保存到 pickle 中"""
     df.to_pickle("output/%s.pkl" % name)
@@ -189,9 +204,31 @@ def generate_activity_line_number(start_time,end_time,top_number):
     fig.savefig("result/activity_line.png")
     plt.close(fig)
 
+def generate_social_line_number(start_time,end_time,top_number):
+    """生成平均值的折线图"""
+    df = pd.read_pickle("output/social_average.pkl")
+    all_df = pd.DataFrame(data=[],index=pd.date_range(start=start_time,end=end_time,freq="W"))
+
+    for index, row in df.iterrows():
+        if len(all_df.columns) < top_number:
+            all_df[row["name"]] = pd.read_pickle("output/social_%s.pkl" % row["name"])["score"]
+        else:
+            break
+
+    fig = all_df.plot().get_figure()
+    fig.savefig("result/social_line.png")
+    plt.close(fig)
+
 def clean_directory():
     """清空临时目录及结果目录"""
     shutil.rmtree('output',ignore_errors=True)
     shutil.rmtree('result',ignore_errors=True)
     click.echo("Workspace is empty now!")
-    pass
+    pass
+
+def is_corp(email,config):
+    """判断是否是企业用户"""
+    if config["corp"]["keyword"] in email:
+        return True
+    else:
+        return False

grank/script/activity.py

@@ -73,15 +73,15 @@ def analyse_repo(owner,repository,data,config):
 
     # 获取平均分实例，用于后续排序
 
-    instance = helpers.get_activity_avarage_instance()
+    instance = helpers.get_activity_average_instance()
 
      # 将项目的活跃分数保存到新的 Pickle 中，用于后续的折线图输出
 
     helpers.series_to_pickle(new_df,repository)
 
     # 对平均分实例进行排序
 
-    helpers.set_activity_avarage(instance,owner,repository,target_score)
+    helpers.set_activity_average(instance,owner,repository,target_score)
 
     # 输出项目的 CSV 数据
     helpers.export_csv(new_df,"%s" % repository)

grank/script/social.py

@@ -0,0 +1,62 @@
+from ..libs import query
+from ..libs import helpers
+import pandas as pd
+import numpy as np
+import click
+import math
+def analyse_repo(owner,repository,data,config):
+    click.echo("分析社区化")
+    pullRequestArray = data["pullRequestArray"]
+    commitArray = data["commitArray"]
+
+    start_time=config["time"]["start_time"]
+    end_time=config["time"]["end_time"]
+    date_range = pd.date_range(start=start_time,end=end_time,freq="W")
+    date_series = pd.Series(np.zeros((len(date_range),), dtype=int),index=date_range)
+
+
+    social_all_frame= pd.DataFrame(commitArray)
+    social_all_frame = social_all_frame[social_all_frame.date != "未标注时间"]
+    social_all_frame["date"] = pd.to_datetime(social_all_frame['date'])
+    for index,row in social_all_frame.iterrows():
+        social_all_frame.loc[index,"author"] = helpers.is_corp(row["author"],config)
+
+    community_df = social_all_frame[social_all_frame.author != True].set_index('date').resample('W')['times'].sum()
+    social_all_df = social_all_frame.set_index('date').resample('W')['times'].sum()
+
+    social_all_df = social_all_df.loc[start_time:end_time]
+    community_df = community_df.loc[start_time:end_time]
+
+    temp_community_series = pd.Series(np.zeros((len(date_range),), dtype=int),index=date_range)
+    temp_social_series = pd.Series(np.zeros((len(date_range),), dtype=int),index=date_range)
+
+    for item in community_df.index:
+        if item in date_series.index:
+            temp_community_series[item] = community_df[item]
+
+    for item in social_all_df.index:
+        if item in date_series.index:
+            temp_social_series[item] = social_all_df[item]
+
+    social_df = pd.DataFrame({
+        "community_member":temp_community_series.values,
+        "all_member":temp_social_series.values,
+    },index = date_range)
+    social_df = social_df.cumsum()
+    social_df["score"] = social_df.apply(lambda row: row.community_member / row.all_member  , axis=1)
+
+    target_social_score = social_df["score"].sum() / len(social_df)
+
+    instance = helpers.get_social_average_instance()
+
+    helpers.series_to_pickle(social_df,"social_%s" % repository)
+
+    helpers.set_social_average(instance,owner,repository,target_social_score)
+
+    helpers.export_csv(social_df,"social_%s" % repository)
+
+    helpers.generate_social_line_number(start_time,end_time,int(config["rank"]["top"]))
+
+    print(target_social_score)
+    exit()
+    pass