Move session scripts to separate directory
This commit is contained in:
Michael Hohn
committed by
=Michael Hohn
=Michael Hohn
parent
582d933130
commit
349d758c14
@@ -1,41 +0,0 @@
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# Experimental work for ../bin/mc-db-unique, to be merged into it.
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import qldbtools.utils as utils
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from pprint import pprint
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import pandas as pd
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# cd ../
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#* Reload CSV file to continue work
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df2 = df_refined = pd.read_csv('scratch/db-info-2.csv')
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# Identify rows missing specific entries
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rows = ( df2['cliVersion'].isna() |
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df2['creationTime'].isna() |
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df2['language'].isna() |
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df2['sha'].isna() )
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df2[rows]
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df3 = df2[~rows]
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df3
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#* post-save work
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df4 = pd.read_csv('scratch/db-info-3.csv')
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# Sort and group
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df_sorted = df4.sort_values(by=['owner', 'name', 'CID', 'creationTime'])
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df_unique = df_sorted.groupby(['owner', 'name', 'CID']).first().reset_index()
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# Find duplicates
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df_dups = df_unique[df_unique['CID'].duplicated(keep=False)]
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len(df_dups)
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df_dups['CID']
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# Set display options
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pd.set_option('display.max_colwidth', None)
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pd.set_option('display.max_columns', None)
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pd.set_option('display.width', 140)
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#
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# Local Variables:
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# python-shell-virtualenv-root: "~/work-gh/mrva/mrvacommander/client/qldbtools/venv/"
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# End:
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#
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@@ -1,61 +0,0 @@
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""" Read a table of CodeQL DB information
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and generate the selection files for
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1. the VS Code CodeQL plugin
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2. the gh-mrva command-line client
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"""
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#
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#* Collect the information and write files
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#
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import pandas as pd
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import sys
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import qldbtools.utils as utils
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import numpy as np
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import importlib
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importlib.reload(utils)
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df0 = pd.read_csv('scratch/db-info-3.csv')
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# Use num_entries, chosen via pseudo-random numbers
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df1 = df0.sample(n=3, random_state=np.random.RandomState(4242))
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repos = []
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for index, row in df1[['owner', 'name', 'CID', 'path']].iterrows():
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owner, name, CID, path = row
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repos.append(utils.form_db_req_name(owner, name, CID))
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repo_list_name = "mirva-list"
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vsc = {
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"version": 1,
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"databases": {
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"variantAnalysis": {
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"repositoryLists": [
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{
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"name": repo_list_name,
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"repositories": repos,
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}
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],
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"owners": [],
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"repositories": []
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}
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},
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"selected": {
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"kind": "variantAnalysisUserDefinedList",
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"listName": repo_list_name
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}
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}
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gh = {
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repo_list_name: repos
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}
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# write the files
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import json
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with open("tmp-selection-vsc.json", "w") as fc:
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json.dump(vsc, fc, indent=4)
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with open("tmp-selection-gh.json", "w") as fc:
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json.dump(gh, fc, indent=4)
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# Local Variables:
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# python-shell-virtualenv-root: "~/work-gh/mrva/mrvacommander/client/qldbtools/venv/"
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# End:
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@@ -1,65 +0,0 @@
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import qldbtools.utils as utils
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import pandas as pd
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import numpy as np
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import sys
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from minio import Minio
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from minio.error import S3Error
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from pathlib import Path
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#
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#* Collect the information and select subset
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#
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df = pd.read_csv('scratch/db-info-2.csv')
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seed = 4242
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if 0:
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# Use all entries
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entries = df
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else:
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# Use num_entries, chosen via pseudo-random numbers
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entries = df.sample(n=3,
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random_state=np.random.RandomState(seed))
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#
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#* Push the DBs
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#
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# Configuration
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MINIO_URL = "http://localhost:9000"
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MINIO_ROOT_USER = "user"
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MINIO_ROOT_PASSWORD = "mmusty8432"
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QL_DB_BUCKET_NAME = "qldb"
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# Initialize MinIO client
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client = Minio(
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MINIO_URL.replace("http://", "").replace("https://", ""),
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access_key=MINIO_ROOT_USER,
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secret_key=MINIO_ROOT_PASSWORD,
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secure=False
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)
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# Create the bucket if it doesn't exist
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try:
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if not client.bucket_exists(QL_DB_BUCKET_NAME):
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client.make_bucket(QL_DB_BUCKET_NAME)
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else:
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print(f"Bucket '{QL_DB_BUCKET_NAME}' already exists.")
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except S3Error as err:
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print(f"Error creating bucket: {err}")
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# (test) File paths and new names
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files_to_upload = {
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"cmd/server/codeql/dbs/google/flatbuffers/google_flatbuffers_db.zip": "google$flatbuffers.zip",
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"cmd/server/codeql/dbs/psycopg/psycopg2/psycopg_psycopg2_db.zip": "psycopg$psycopg2.zip"
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}
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# (test) Push the files
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prefix = Path('/Users/hohn/work-gh/mrva/mrvacommander')
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for local_path, new_name in files_to_upload.items():
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try:
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client.fput_object(QL_DB_BUCKET_NAME, new_name, prefix / Path(local_path))
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print(f"Uploaded {local_path} as {new_name} to bucket {QL_DB_BUCKET_NAME}")
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except S3Error as err:
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print(f"Error uploading file {local_path}: {err}")
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# Local Variables:
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# python-shell-virtualenv-root: "~/work-gh/mrva/mrvacommander/client/qldbtools/venv/"
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# End:
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@@ -1,45 +0,0 @@
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import qldbtools.utils as utils
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import pandas as pd
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#
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#* Collect the information
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#
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df1 = pd.read_csv("scratch/db-info-2.csv")
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# Add single uniqueness field -- CID (Cumulative ID) -- using
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# - creationTime
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# - sha
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# - cliVersion
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# - language
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from hashlib import blake2b
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def cid_hash(row_tuple: tuple):
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"""
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cid_hash(row_tuple)
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Take a bytes object and return hash as hex string
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"""
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h = blake2b(digest_size = 3)
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h.update(str(row_tuple).encode())
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# return int.from_bytes(h.digest(), byteorder='big')
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return h.hexdigest()
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# Apply the cid_hash function to the specified columns and create the 'CID' column
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df1['CID'] = df1.apply(lambda row: cid_hash( (row['creationTime'],
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row['sha'],
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row['cliVersion'],
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row['language'])
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), axis=1)
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df2 = df1.reindex(columns=['owner', 'name', 'cliVersion', 'creationTime',
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'language', 'sha','CID', 'baselineLinesOfCode', 'path',
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'db_lang', 'db_lang_displayName', 'db_lang_file_count',
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'db_lang_linesOfCode', 'ctime', 'primaryLanguage',
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'finalised', 'left_index', 'size'])
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df1['cid']
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# Local Variables:
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# python-shell-virtualenv-root: "~/work-gh/mrva/mrvacommander/client/qldbtools/venv/"
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# End:
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@@ -1,59 +0,0 @@
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#* Experimental work with utils.py, to be merged into it.
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# The rest of this interactive script is available as cli script in
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# mc-db-initial-info
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from utils import *
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#* Data collection
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# Get the db information in list of DBInfo form
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db_base = "~/work-gh/mrva/mrva-open-source-download/"
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dbs = list(collect_dbs(db_base))
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# Inspect:
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from pprint import pprint
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pprint(["len", len(dbs)])
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pprint(["dbs[0]", dbs[0].__dict__])
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pprint(["dbs[-1]", dbs[-1].__dict__])
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#
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# Get a dataframe
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dbdf = pd.DataFrame([d.__dict__ for d in dbs])
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#
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#* Experiments with on-disk format
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# Continue use of raw information in separate session.
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#
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# PosixPath is a problem for json and parquet
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#
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dbdf['path'] = dbdf['path'].astype(str)
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#
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dbdf.to_csv('dbdf.csv')
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#
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dbdf.to_csv('dbdf.csv.gz', compression='gzip', index=False)
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#
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dbdf.to_json('dbdf.json')
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#
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# dbdf.to_hdf('dbdf.h5', key='dbdf', mode='w')
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#
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# fast, binary
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dbdf.to_parquet('dbdf.parquet')
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#
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# fast
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import sqlite3
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conn = sqlite3.connect('dbdf.db')
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dbdf.to_sql('qldbs', conn, if_exists='replace', index=False)
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conn.close()
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#
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# Sizes:
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# ls -laSr dbdf.*
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# -rw-r--r--@ 1 hohn staff 101390 Jul 12 14:17 dbdf.csv.gz
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# -rw-r--r--@ 1 hohn staff 202712 Jul 12 14:17 dbdf.parquet
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# -rw-r--r--@ 1 hohn staff 560623 Jul 12 14:17 dbdf.csv
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# -rw-r--r--@ 1 hohn staff 610304 Jul 12 14:17 dbdf.db
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# -rw-r--r--@ 1 hohn staff 735097 Jul 12 14:17 dbdf.json
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#
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# parquet has many libraries, including go: xitongsys/parquet-go
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# https://parquet.apache.org/
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#
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# Local Variables:
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# python-shell-virtualenv-root: "~/work-gh/mrva/mrvacommander/client/qldbtools/venv/"
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# End:
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@@ -1,118 +0,0 @@
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# Experimental work with utils.py, to be merged into it.
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from utils import *
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from pprint import pprint
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#* Reload gzipped CSV file to continue work
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dbdf_1 = pd.read_csv('dbdf.csv.gz', compression='gzip')
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#
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# (old) Consistency check:
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# dbdf_1.columns == dbdf.columns
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# dbmask = (dbdf_1 != dbdf)
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# dbdf_1[dbmask]
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# dbdf_1[dbmask].dropna(how='all')
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# ctime_raw is different in places, so don't use it.
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#
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#* Interact with/visualize the dataframe
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# Using pandasgui -- qt
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from pandasgui import show
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os.environ['APPDATA'] = "needed-for-pandasgui"
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show(dbdf_1)
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# Using dtale -- web
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import dtale
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dtale.show(dbdf_1)
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#
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#
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#* Collect metadata from DB zip files
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#
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#** A manual sample
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#
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d = dbdf_1
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left_index = 0
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d.path[0]
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cqlc, metac = extract_metadata(d.path[0])
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cqlc['baselineLinesOfCode']
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cqlc['primaryLanguage']
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cqlc['creationMetadata']['sha']
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cqlc['creationMetadata']['cliVersion']
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cqlc['creationMetadata']['creationTime'].isoformat()
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cqlc['finalised']
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for lang, lang_cont in metac['languages'].items():
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print(lang)
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indent = " "
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for prop, val in lang_cont.items():
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if prop == 'files':
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print("%sfiles count %d" % (indent, len(val)))
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elif prop == 'linesOfCode':
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print("%slinesOfCode %d" % (indent, val))
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elif prop == 'displayName':
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print("%sdisplayName %s" % (indent, val))
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#** Automated for all entries
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# The rest of this interactive script is available as cli script in
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# mc-db-refine-info
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d = dbdf_1
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joiners = []
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for left_index in range(0, len(d)-1):
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try:
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cqlc, metac = extract_metadata(d.path[left_index])
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except ExtractNotZipfile:
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continue
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except ExtractNoCQLDB:
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continue
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try:
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detail_df = metadata_details(left_index, cqlc, metac)
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except DetailsMissing:
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continue
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joiners.append(detail_df)
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joiners_df = pd.concat(joiners, axis=0)
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full_df = pd.merge(d, joiners_df, left_index=True, right_on='left_index', how='outer')
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#** View the full dataframe with metadata
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from pandasgui import show
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os.environ['APPDATA'] = "needed-for-pandasgui"
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show(full_df)
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#** Re-order the dataframe columns by importance
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# - Much of the data
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# 1. Is only conditionally present
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# 2. Is extra info, not for the DB proper
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# 3. May have various names
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# - The essential columns are
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# | owner |
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# | name |
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# | language |
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# | size |
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# | cliVersion |
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# | creationTime |
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# | sha |
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# | baselineLinesOfCode |
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# | path |
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# - The rest are useful; put them last
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# | db_lang |
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# | db_lang_displayName |
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# | db_lang_file_count |
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# | db_lang_linesOfCode |
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# | left_index |
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# | ctime |
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# | primaryLanguage |
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# | finalised |
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final_df = full_df.reindex(columns=['owner', 'name', 'language', 'size', 'cliVersion',
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'creationTime', 'sha', 'baselineLinesOfCode', 'path',
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'db_lang', 'db_lang_displayName', 'db_lang_file_count',
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'db_lang_linesOfCode', 'ctime', 'primaryLanguage',
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'finalised', 'left_index'])
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final_df.to_csv('all-info-table.csv.gz', compression='gzip', index=False)
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#
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# Local Variables:
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# python-shell-virtualenv-root: "~/work-gh/mrva/mrvacommander/client/qldbtools/venv/"
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# End:
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#
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Reference in New Issue
Block a user