68 lines
3.6 KiB
Python
68 lines
3.6 KiB
Python
import csv
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import os
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import pandas as pd
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def for_readme_files():
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ld_csv_path = "final_data/deb_readme_did.csv"
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ta_csv_path = "text_analysis/d_readability_readme.csv"
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topic_csv_path = "text_analysis/readme_file_topic_distributions.csv"
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# criteria for good readme
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# mean of three examples listed on the contributing page
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# https://github.com/rails/rails/blob/main/README.md (71s)
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# https://github.com/github/docs/blob/main/README.md (48s)
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# https://github.com/opengovernment/opengovernment/blob/master/README.md (14s)
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# Median 48s
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readme_ta_df = pd.read_csv(ta_csv_path)
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time_threshold_df = readme_ta_df[readme_ta_df['reading_time'] >= 48]
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# R8 > 0.10 (0.8 * 0.125)
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readme_topic_df = pd.read_csv(topic_csv_path)
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topic_threshold_df = readme_topic_df[readme_topic_df['t7'] >= 0.1]
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readme_exemplar_df = pd.merge(time_threshold_df, topic_threshold_df, on="filename", how="inner")
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exemplary_files = readme_exemplar_df['filename'].str.split('_').str[:-1].str.join("_")
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#one manual cleaning
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exemplary_files = exemplary_files[exemplary_files!= "CheMPS2_README_8md"]
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exemplary_files = pd.concat([exemplary_files, pd.Series("CheMPS2")])
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print(len(exemplary_files))
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#connecting with the timeseries data
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ts_data_df = pd.read_csv(ld_csv_path)
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#print(ts_data_df['upstream_vcs_link'].str.split("/").str[-1])
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test_vec = ts_data_df['upstream_vcs_link'].str.split("/").str[-1]
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diff_vec = exemplary_files[~exemplary_files.isin(test_vec)]
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subset_ts_data = ts_data_df[ts_data_df['upstream_vcs_link'].str.split("/").str[-1].isin(exemplary_files)]
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#if "CheMPS2" in exemplary_files:
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# subset_ts_data = pd.concat([subset_ts_data, ts_data_df[ts_data_df['upstream_vcs_link'] == "https://github.com/SebWouters/CheMPS2"]])
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print(subset_ts_data.shape[0])
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#print("https://github.com/SebWouters/CheMPS2" in subset_ts_data["upstream_vcs_link"])
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#subset_ts_data.to_csv('102724_readme_exemplar_subset.csv', index=False)
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def for_contributing_files():
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ld_csv_path = "final_data/deb_contrib_did.csv"
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ta_csv_path = "text_analysis/d_readability_contributing.csv"
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topic_csv_path = "text_analysis/contrib_file_topic_distributions.csv"
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# criteria for good contributing
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# mean of three examples listed on https://docs.github.com/en/communities/setting-up-your-project-for-healthy-contributions/setting-guidelines-for-repository-contributors
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# https://github.com/rails/rails/blob/main/CONTRIBUTING.md (116s)
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# https://github.com/github/docs/blob/main/.github/CONTRIBUTING.md (59s)
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# https://github.com/opengovernment/opengovernment/blob/master/CONTRIBUTING.md (45s)
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# Median 59s
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contributing_ta_df = pd.read_csv(ta_csv_path)
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time_threshold_df = contributing_ta_df[contributing_ta_df['reading_time'] >= 59]
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# and then making sure they're on topic, C4 > 0.2 (0.8 * 0.25)
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contributing_topic_df = pd.read_csv(topic_csv_path)
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topic_threshold_df = contributing_topic_df[contributing_topic_df['t3'] >= 0.2]
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contributing_exemplar_df = pd.merge(topic_threshold_df, time_threshold_df, on='filename', how='inner')
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print(contributing_exemplar_df.shape[0])
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exemplary_files = contributing_exemplar_df['filename'].str.replace("_CONTRIBUTING.md", "")
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# reading in the data
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ts_data_df = pd.read_csv(ld_csv_path)
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subset_ts_data = ts_data_df[ts_data_df['upstream_vcs_link'].str.split("/").str[-1].isin(exemplary_files)]
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print(subset_ts_data.shape[0])
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#subset_ts_data.to_csv('102724_contrib_exemplar_subset.csv', index=False)
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if __name__ == "__main__":
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for_contributing_files()
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for_readme_files() |