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#!/usr/bin/env python
"""
Elasticsearch CRUD operations using requests library
"""
import json
import requests
# Configuration
ES_HOST = "localhost"
ES_PORT = 9200
ES_URL = f"http://{ES_HOST}:{ES_PORT}"
INDEX_NAME = "products"
# HTTP session (security disabled: no authentication)
session = requests.Session()
sessio... | veltzer/demos-db-elk | exercises/developer/00_crud/16_requests_create_index.py | .py | ccb58f40512df757 | 7.15 | 1 |
#!/usr/bin/env python
"""
Elasticsearch CRUD operations using requests library
"""
import json
import requests
# Configuration
ES_HOST = "localhost"
ES_PORT = 9200
ES_URL = f"http://{ES_HOST}:{ES_PORT}"
INDEX_NAME = "products"
# HTTP session (security disabled: no authentication)
session = requests.Session()
sessio... | veltzer/demos-db-elk | exercises/developer/00_crud/18_requests_search_documents.py | .py | f081a4f4fe6eefc7 | 7.15 | 1 |
#!/usr/bin/env python
"""
Elasticsearch CRUD operations using requests library
"""
import json
from datetime import UTC, datetime
import requests
# Configuration
ES_HOST = "localhost"
ES_PORT = 9200
ES_URL = f"http://{ES_HOST}:{ES_PORT}"
INDEX_NAME = "products"
# HTTP session (security disabled: no authentication)
... | veltzer/demos-db-elk | exercises/developer/00_crud/19_requests_update_documents.py | .py | 106f0616543b0020 | 7.15 | 1 |
#!/usr/bin/env python
"""
Elasticsearch CRUD operations using requests library
"""
import json
import requests
# Configuration
ES_HOST = "localhost"
ES_PORT = 9200
ES_URL = f"http://{ES_HOST}:{ES_PORT}"
INDEX_NAME = "products"
# HTTP session (security disabled: no authentication)
session = requests.Session()
sessio... | veltzer/demos-db-elk | exercises/developer/00_crud/20_requests_delete_documents.py | .py | 86090c94470a6230 | 7.15 | 1 |
#!/usr/bin/env python
"""
Update documents in the products index using the Elasticsearch client
"""
from datetime import UTC, datetime
from elasticsearch import Elasticsearch
# Initialize client
es = Elasticsearch(
['http://localhost:9200'],
)
INDEX_NAME = "products"
def update_document_full(doc_id: str):
... | veltzer/demos-db-elk | exercises/developer/00_crud/25_client_update_documents.py | .py | 3f8b88d600a68c96 | 7.15 | 1 |
#!/usr/bin/env python
"""
Delete documents from the products index using the Elasticsearch client
"""
from elastic_transport import TransportError
from elasticsearch import ApiError, Elasticsearch
# Initialize client
es = Elasticsearch(
['http://localhost:9200'],
)
INDEX_NAME = "products"
def delete_document(do... | veltzer/demos-db-elk | exercises/developer/00_crud/26_client_delete_documents.py | .py | 7e4f521b085d1852 | 7.15 | 1 |
#!/usr/bin/env python
"""
Advanced CRUD operations (mget, bulk, scroll, count) using the Elasticsearch client
"""
from elasticsearch import Elasticsearch
# Initialize client
es = Elasticsearch(
['http://localhost:9200'],
)
INDEX_NAME = "products"
def mget_documents():
"""Get multiple documents in one reques... | veltzer/demos-db-elk | exercises/developer/00_crud/27_client_advanced_operations.py | .py | 7cb6d178baaaf9a1 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
from pyspark import SparkConf, SparkContext
# Initialize Spark
conf = SparkConf().setAppName("Simple RDD Example").setMaster("local[*]")
sc = SparkContext(conf=conf)
# sc.setLogLevel("ERROR")
# Create RDD from a list
# numbers = [1, 2, 3, 4, 5]
# this is called a python "gene... | veltzer/demos-bd-spark | exercises/pyspark/04_rdd_basics/solution.py | .py | f8942a902f6b08a5 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import os
from pyspark import SparkConf, SparkContext
def create_spark():
"""Create and configure SparkContext"""
conf = SparkConf().setAppName("Text Processing").setMaster("spark://localhost:7077")
return SparkContext(conf=conf)
def analyze_files(sc, input_dir):... | veltzer/demos-bd-spark | exercises/pyspark/07_rdd_many_files/solution.py | .py | b725fc405b8f206f | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import time
from pyspark.sql import SparkSession
# Create Spark session
spark = SparkSession.builder.appName("ShuffleOptimization").getOrCreate()
# Create sample data
# Orders data with customer_id and amount
orders_data = [(i, i % 100, float(i * 10)) for i in range(1000000)]... | veltzer/demos-bd-spark | exercises/pyspark/11_sql_shuffle/solution.py | .py | b68c3baf4ab66970 | 7.15 | 1 |
#!/usr/bin/env python
"""
Chaining
"""
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SimpleOptimization").getOrCreate()
sc = spark.sparkContext
# Create sample data
numbers = range(1, 100000000)
rdd = sc.parallelize(numbers)
# Inefficient way: Multiple separate operations
def inefficie... | veltzer/demos-bd-spark | exercises/pyspark/12_shuffle/chaining.py | .py | 40a1489b777944cb | 7.15 | 1 |
#!/usr/bin/env python
"""
Explain output
"""
import pyspark.sql.functions as F
from pyspark.sql import SparkSession
def show_plan(df, title):
""" Function to capture and print explain plan """
print(f"{title}")
print("=" * len(title))
# Get the plan explanation
plan = []
# pylint: disable=pr... | veltzer/demos-bd-spark | exercises/pyspark/12_shuffle/explain-output.py | .py | e9af94c4daa93ed0 | 7.15 | 1 |
#!/usr/bin/env python
"""
Explain
"""
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SimpleOptimization").getOrCreate()
sc = spark.sparkContext
numbers = range(1, 1000)
rdd = sc.parallelize(numbers)
# Convert RDD operations to DataFrame operations so we can use explain()
df = spark.creat... | veltzer/demos-bd-spark | exercises/pyspark/12_shuffle/explain.py | .py | 9e28d7cef411991e | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("OperationCounter").getOrCreate()
sc = spark.sparkContext
numbers = range(1, 10000)
df = spark.createDataFrame([(x,) for x in numbers], ["value"])
# Create accumulators to count operations
filter_ops = s... | veltzer/demos-bd-spark | exercises/pyspark/12_shuffle/ff.py | .py | a13cf2de64b6ca89 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SimpleOptimization").getOrCreate()
sc = spark.sparkContext
# Create sample data
numbers = range(1, 100000)
rdd = sc.parallelize(numbers)
# Inefficient way: Multiple separate operations
def inefficient_... | veltzer/demos-bd-spark | exercises/pyspark/12_shuffle/solution.py | .py | e0c0236241e9bd29 | 7.15 | 1 |
#!/usr/bin/env python
"""
optimized version
"""
import time
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SimpleOptimization").getOrCreate()
sc = spark.sparkContext
# Create sample data
numbers = range(1, 10000000)
rdd = sc.parallelize(numbers)
# Inefficient way: Multiple separate ope... | veltzer/demos-bd-spark | exercises/pyspark/13_rdd_opt/opt.py | .py | a8892ec640fdeb33 | 7.15 | 1 |
#!/usr/bin/env python
"""
Example
"""
import os
import time
from pyspark.sql import SparkSession
# Create Spark session
cdir = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
spark = SparkSession.builder.appName(cdir).getOrCreate()
sc = spark.sparkContext
# Create a large RDD
data = range(1, 10000000)... | veltzer/demos-bd-spark | exercises/pyspark/14_rdd_cache/example.py | .py | 41572dcd816568a2 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import time
from pyspark.sql import SparkSession
# Create Spark session
spark = SparkSession.builder.appName("SQLCachingDemo").getOrCreate()
# Create sample data
n_rows = 1000000
data = [(i, f"product_{i % 1000}", i * 10) for i in range(n_rows)]
df = spark.createDataFrame(dat... | veltzer/demos-bd-spark | exercises/pyspark/15_rdd_cache/solution.py | .py | 7a38e16e8ec001d3 | 7.15 | 1 |
#!/usr/bin/env python
"""
Example
"""
import time
from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast
# Create Spark session
spark = SparkSession.builder.appName("BroadcastJoinDemo").getOrCreate()
# Create a large dataframe
large_data = [(i, f"product_{i % 1000}", i * 10) for i in rang... | veltzer/demos-bd-spark | exercises/pyspark/16_broadcast_join/example.py | .py | fc47f3c25b68bbdb | 7.15 | 1 |
#!/usr/bin/env python
"""
Example
"""
import random
import time
from pyspark.sql import SparkSession
from pyspark.sql.functions import count
from pyspark.sql.functions import sum as sql_sum
def create_skewed_data(num_rows, num_keys, skew_factor):
""" Create sample data with skewed keys """
data = []
fo... | veltzer/demos-bd-spark | exercises/pyspark/17_two_phase_aggregation/example.py | .py | 73fadd40cf759824 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution from file
"""
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType
def create_spark_session():
"""Create and return a Spark session"""
return SparkSession.builder \
.appName("IP Address Analysis") \
... | veltzer/demos-bd-spark | exercises/pyspark/18_udf/solution_from_file.py | .py | 9b7a3c980b414693 | 7.15 | 1 |
#!/usr/bin/env python
"""
Dashboard
"""
import os
import plotly.express as px
import streamlit as st
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, date_format, desc, to_date
from pyspark.sql.functions import round as my_round
from pyspark.sql.functions import sum as my_sum
# Initialize... | veltzer/demos-bd-spark | exercises/pyspark/19_reports/dashboard.py | .py | 33ce95c181c71dd2 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import logging
from datetime import datetime
from pyspark.sql import SparkSession
from pyspark.sql.functions import (
avg,
col,
count,
countDistinct,
date_format,
desc,
months_between,
to_date,
when,
)
from pyspark.sql.functions import max as... | veltzer/demos-bd-spark | exercises/pyspark/19_reports/solution.py | .py | 7651e7a8b2329709 | 7.15 | 1 |
#!/usr/bin/env python
"""
Generate data
"""
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
def generate_sales_data(num_stores=5, days=30, seed=42):
"""Generate sample sales data for the window functions exercise."""
np.random.seed(seed)
# Generate dates
start_date... | veltzer/demos-bd-spark | exercises/pyspark/20_window/generate.py | .py | 724c095a8939ca5f | 7.15 | 1 |
#!/usr/bin/env python
"""
Exercise 1: RDD Caching and Persistence
This script performs multiple operations on the same RDD without caching,
causing repeated computations. Your task is to optimize it using appropriate
caching/persistence strategies.
"""
import time
from pyspark.sql import SparkSession
def create_s... | veltzer/demos-bd-spark | exercises/pyspark/opt_caching/exercise.py | .py | c3a2c0b8324fadac | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import time
from pyspark.sql import SparkSession
def create_spark_session():
return SparkSession.builder \
.appName("RDD Caching Exercise") \
.getOrCreate()
def generate_large_dataset(spark, size=10000):
"""Generate a large dataset with repeated value... | veltzer/demos-bd-spark | exercises/pyspark/opt_caching/solution.py | .py | ec123f1373728fb0 | 7.15 | 1 |
#!/usr/bin/env python
"""
Create data
"""
import random
from datetime import datetime, timedelta
from pyspark.sql import SparkSession
# Initialize Spark
spark = SparkSession.builder \
.appName("Explain Examples Data Setup") \
.enableHiveSupport() \
.getOrCreate()
# Create large table for meaningful exp... | veltzer/demos-bd-spark | exercises/pyspark/opt_explain/create_data.py | .py | 3b4541e55aa4ea07 | 7.15 | 1 |
#!/usr/bin/env python
"""
Simplified PySpark Repartitioning Exercise - Naive Query Script
This script performs a category/region aggregation on the skewed transactions dataset
WITHOUT proper repartitioning, demonstrating performance challenges with skewed data.
"""
import os
import sys
import time
from pyspark.sql i... | veltzer/demos-bd-spark | exercises/pyspark/opt_repartition/simplified-naive-query.py | .py | adff366ab176bc96 | 7.15 | 1 |
#!/usr/bin/env python
"""
Fixed Optimized Query Script - Excluding Repartitioning Time
This script performs aggregation with repartitioning but properly
handles partition information collection to avoid errors.
"""
import os
import sys
import time
from pyspark.sql import SparkSession
from pyspark.sql.functions impor... | veltzer/demos-bd-spark | exercises/pyspark/opt_repartition/simplified-optimized-query.py | .py | a8de1ce47436c593 | 7.15 | 1 |
#!/usr/bin/env python
"""
Sort-Merge Performance Exercise - Comparison Script
This script compares the performance results from the naive and optimized
solutions and generates visualization charts.
"""
import json
import os
import sys
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
... | veltzer/demos-bd-spark | exercises/pyspark/opt_sort_merge/comparison-script.py | .py | 82e03be6ad672ffe | 7.15 | 1 |
#!/usr/bin/env python
"""
Sort-Merge Performance Exercise - Fixed Data Generation Script
This script generates two datasets (transactions and products) in both
sorted and unsorted versions for performance comparison, ensuring
proper global sorting of the sorted datasets.
"""
import os
import shutil
import time
from ... | veltzer/demos-bd-spark | exercises/pyspark/opt_sort_merge/data-generation-script.py | .py | 00daab7df804bd1f | 7.15 | 1 |
#!/usr/bin/env python
"""
Sort-Merge Performance Exercise - Naive Solution
This script demonstrates joining unsorted datasets, requiring Spark
to perform sorting during the join operation.
"""
import json
import os
import sys
import time
from pyspark.sql import SparkSession
# Initialize Spark Session
spark = SparkS... | veltzer/demos-bd-spark | exercises/pyspark/opt_sort_merge/naive-solution.py | .py | ea1df842e08324da | 7.15 | 1 |
#!/usr/bin/env python
"""
Sort-Merge Performance Exercise - Optimized Solution
This script demonstrates joining pre-sorted datasets, allowing Spark
to skip the sorting step during the join operation.
"""
import json
import os
import sys
import time
from pyspark.sql import SparkSession
# Initialize Spark Session
spa... | veltzer/demos-bd-spark | exercises/pyspark/opt_sort_merge/optimized-solution.py | .py | 2403a0444a7b4878 | 7.15 | 1 |
#!/usr/bin/env python
"""
Exercise
"""
import random
from datetime import datetime, timedelta
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
def create_spark_session():
return SparkSession.builder \
.appName("Partition Pruning Exercise") \
.config("spark.sql.sources... | veltzer/demos-bd-spark | exercises/pyspark/opt_sql_prunning/exercise.py | .py | 6f0ad21aeaf04eaf | 7.15 | 1 |
#!/usr/bin/env python
"""
PySpark Data Generation Script for ANALYZE TABLE Exercise
This script creates two tables with skewed data distribution:
1. orders - A large fact table with order information
2. customers - A dimension table with customer details
"""
import random
from pyspark.sql import SparkSession
from py... | veltzer/demos-bd-spark | exercises/pyspark/opt_statistics/data-prep.py | .py | a84c73f6485bdb58 | 7.15 | 1 |
#!/usr/bin/env python
"""
Slow Query Script - Without ANALYZE TABLE
This script executes a complex query that joins the customers and orders tables
with multiple filters. Without table statistics, Spark makes suboptimal decisions
about join strategies, predicate pushdown, and partition pruning.
"""
import sys
import... | veltzer/demos-bd-spark | exercises/pyspark/opt_statistics/exercise.py | .py | 8f5f8f6b71e090a0 | 7.15 | 1 |
#!/usr/bin/env python
"""
Optimized Query Script - WITH ANALYZE TABLE
This script runs the exact same query as slow_query.py, but first collects
statistics using ANALYZE TABLE. With these statistics, Spark can make
better decisions about join strategies, predicate pushdown, and partition pruning.
"""
import sys
impo... | veltzer/demos-bd-spark | exercises/pyspark/opt_statistics/solution.py | .py | 6407b2a59137ae80 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import time
from pyspark.sql import SparkSession
# Initialize Spark Session
spark = SparkSession.builder \
.appName("Statistics Exercise - Optimized Query") \
.getOrCreate()
def compute_statistics():
print("Computing table statistics...")
# Compute table-leve... | veltzer/demos-bd-spark | exercises/pyspark/opt_statistics_old/solution.py | .py | 18c83b39068c3d0a | 7.15 | 1 |
#!/usr/bin/env python
"""
Exercise 2: Optimizing Count Distinct with Two-Phase Aggregation
This exercise demonstrates the performance difference between regular
count distinct and a two-phase approach with partition-level aggregation.
"""
import random
import time
import pyspark.sql.functions as F
from pyspark.sql ... | veltzer/demos-bd-spark | exercises/pyspark/opt_two_phase/exercise.py | .py | ff4804d2b961a320 | 7.15 | 1 |
#!/usr/bin/env python
"""
Solution
"""
import hashlib
import random
import time
import pyspark.sql.functions as F
from pyspark.sql import SparkSession
from pyspark.sql.types import StringType
def create_spark_session():
return SparkSession.builder \
.appName("Count Distinct Exercise") \
.config... | veltzer/demos-bd-spark | exercises/pyspark/opt_two_phase/solution.py | .py | 060de53636ab096f | 7.15 | 1 |
"""Application-wide constants and filesystem paths.
Data lives outside the installation directory so that shop data survives
application updates and works when the app is installed to a read-only
location (Program Files, /Applications). Dropping a file named
``portable.txt`` next to the executable switches the app to... | devShakib015/BusinessMonitoringApp | app/config.py | .py | 08a1f7524a3a5b89 | 7 | 0 |
"""Timestamps.
The shop's computer clock is the source of truth: a shopkeeper reading a
day-end report means *their* day, and the till is not shared across
timezones. Storing local time keeps date-range reports simple and keeps the
stored value readable when someone opens the database directly.
"""
from datetime imp... | devShakib015/BusinessMonitoringApp | app/core/clock.py | .py | 6155cc3dcb0352c4 | 7 | 0 |
"""SQLite access layer.
One connection is shared for the life of the process, guarded by a re-entrant
lock so a background export cannot interleave with a checkout. WAL mode keeps
reads from blocking the write that commits a sale.
"""
import sqlite3
import threading
from contextlib import contextmanager
from app im... | devShakib015/BusinessMonitoringApp | app/core/db.py | .py | 7413b11e43f46de9 | 7 | 0 |
"""Money handling.
Every amount in the database is stored as an **integer number of minor units**
(paisa, cents, fils...). Floating point is never used for money: 0.1 + 0.2 is
not 0.3, and a till that is off by a paisa a hundred times a day is a till the
shopkeeper stops trusting.
The number of decimals is a shop se... | devShakib015/BusinessMonitoringApp | app/core/money.py | .py | 8766a8a299fcff8d | 7 | 0 |
"""Quantities are stored as integer thousandths.
A shop that sells rice by the kilo needs ``1.5`` to be a real quantity, and a
shop that sells bottles needs ``2`` to print as ``2`` and not ``2.000``. One
integer scale covers both without floats creeping into stock arithmetic.
"""
from decimal import Decimal, ROUND_H... | devShakib015/BusinessMonitoringApp | app/core/quantity.py | .py | 42f60503ee417c4b | 7 | 0 |
"""Password hashing and session state.
Passwords are stored as salted PBKDF2-HMAC-SHA256 digests. The previous
version of this app kept them in plain text in the SQLite file, which meant
anyone who could copy ``main.db`` had the owner's password.
"""
import hashlib
import hmac
import os
import secrets
from dataclass... | devShakib015/BusinessMonitoringApp | app/core/security.py | .py | c66a73ba91094215 | 7 | 0 |
"""Append-only audit trail for actions worth answering questions about later."""
from contextlib import contextmanager
from app.core import clock, db
_muted = False
@contextmanager
def muted():
"""Stop recording — used while seeding sample data, which is not real work."""
global _muted
previous, _muted... | devShakib015/BusinessMonitoringApp | app/repo/activity.py | .py | a57e596bcfc51f67 | 7 | 0 |
"""Customers and their outstanding balance.
A sale does not need a customer -- a walk-in is the common case and attaching
one is optional. Customers exist so a shop can put a sale "on the book" and
collect later, which is how most neighbourhood shops actually trade.
"""
from app.core import clock, db
_BALANCE = """... | devShakib015/BusinessMonitoringApp | app/repo/customers.py | .py | a6ef18a3cc9d15c4 | 7 | 0 |
"""Backups.
Shop data is the shop's livelihood, so backing it up is a one-click action in
the app rather than a folder the owner is expected to know about. Copies are
made with SQLite's online backup API, which is safe while the database is open
and in WAL mode (a plain file copy is not).
"""
import os
import shutil... | devShakib015/BusinessMonitoringApp | app/services/backup.py | .py | 1583ef5a89529a52 | 7 | 0 |
"""Returns and refunds.
A shop that cannot take something back is not running a till, it is running a
one-way door. A return re-stocks what came back, refunds in cash or credits
the customer's account, and never lets more come back than went out.
"""
from dataclasses import dataclass
from app.core import clock, db,... | devShakib015/BusinessMonitoringApp | app/services/returns.py | .py | cd533ad7242799ed | 7 | 0 |
"""Common behaviour for the screens in the main window."""
from PySide6.QtWidgets import QWidget
from app.core.security import Session
from app.ui.widgets.common import vbox
class Page(QWidget):
"""A screen in the sidebar.
``refresh`` runs every time the page becomes visible, so a sale made on
the sell... | devShakib015/BusinessMonitoringApp | app/ui/pages/base.py | .py | 0290f3dd7cb773ca | 7 | 0 |
"""YAML config loading and validation for SegCraft."""
from __future__ import annotations
from importlib.resources import as_file, files
from pathlib import Path
from typing import Any, Dict
from .schema import SegCraftConfig, parse_config, validate_config
def _load_yaml(path: Path) -> Dict[str, Any]:
try:
... | oney-erge/SegCraft-Semantic-Segmentation | src/segcraft/config/loader.py | .py | 971fd8716384985b | 7.24 | 2 |
"""Build a unit from scratch in Python: a GondorFighter horde, its members,
their weapon, and the command buttons that drive it.
`sage_ini`'s model is parse-driven - a typed `Object`/`Weapon`/`CommandButton`
is built from an AST `Block`, not from a constructor. So "creating a unit purely
from Python" means assembling ... | ClementJ18/pySAGE | examples/sage_ini/build_gondor_fighter_horde.py | .py | 047aeb82fbef056f | 7.35 | 4 |
"""How much damage does MordorFighter do to GondorFighter per hit?
Loads the base game, takes the attacker's weapon (the nuggets on its primary
weapon) and applies each DamageNugget to the defender's armor: the damage dealt
for a nugget is `nugget.Damage * armor.get_damage_scalar(nugget.DamageType)`.
Run from the rep... | ClementJ18/pySAGE | examples/sage_ini/damage_calc.py | .py | 68ca8f91ec550b40 | 7.35 | 4 |
"""A turn-by-turn duel: who dies first, MordorFighter or GondorFighter?
Each unit's health is the `MaxHealth` of its body module (`ActiveBody`). The two
units trade single blows; each blow subtracts the attacker's per-hit damage
(weapon nuggets vs the defender's armor, see damage_calc.py) from the defender's
remaining... | ClementJ18/pySAGE | examples/sage_ini/duel.py | .py | 074bc607b4abd1b5 | 7.35 | 4 |
"""Shared setup for the `sage_live` examples: attach, order, and name the local player's units.
Every example needs the same few things - a writable handle on the running game, the local
player, and some units to order about - so they live here rather than being copied four times.
The attaching itself is `sage_live.a... | ClementJ18/pySAGE | examples/sage_live/_common.py | .py | 4b72a8af6c176b94 | 7.35 | 4 |
"""Keep the camera on wherever the fighting is, so a bot's match can be filmed unattended.
The camera half of the live bridge, doing the job it exists for. Each cycle this reads the
whole board, decides where the action is, and eases the camera toward it - no orders are sent
and nothing about the match is touched, so ... | ClementJ18/pySAGE | examples/sage_live/follow_action.py | .py | 4b26f83f94ae6168 | 7.35 | 4 |
"""How fast does `ParticleSystemManager` actually step, against the client frame?
`[TheParticleSystemManager+0x74]` is the stamp the update gate compares. Stock writes the raw
client frame there, so particle systems take one step per client frame and every effect in the
game runs at the frame rate - `render-rate.md` §... | ClementJ18/pySAGE | examples/sage_live/fx_step.py | .py | 86499fb1a6a8d23c | 7.35 | 4 |
"""Does the GPU particle clock advance at the authored 30, or at the client rate?
`Type = GPU_PARTICLE` systems never go through the gate `render-rate.md` §9.4 installs. They read
the W3D millisecond clock at `0x00DD1E0C` and convert it to frames with the *live* client rate -
`ms * clientRate * 0.001` - at four sites ... | ClementJ18/pySAGE | examples/sage_live/gpu_particle_clock.py | .py | 4a131cfa0409e656 | 7.35 | 4 |
"""Order a battalion **while it is still forming**, and watch whether it comes apart.
The bug this reproduces: a horde given an order before it has finished coming out of the
building that made it can end up as loose units that nothing can attack - only trample or
splash damage touches them. `sage_patch/docs/horde-for... | ClementJ18/pySAGE | examples/sage_live/horde_formation.py | .py | 6ebfddf664cc4a6a | 7.35 | 4 |
"""Flip a running game between client rates, so one match can be measured at both.
`render_rate_probe.py` compares 30 fps against 60. Doing that by patching `game.dat` and
relaunching gives two *different* matches - different units, different camera, different
motion - and the numbers that matter (distinct interpolate... | ClementJ18/pySAGE | examples/sage_live/set_render_rate.py | .py | 225abf9d9a97afe4 | 7.35 | 4 |
"""Read-only reconnaissance of a live match: everything a bot needs to know before it acts.
Run this once at the start of a match, before writing any policy against it. It answers the
four questions that decide whether a bot can play at all, and it answers them by *measuring*
rather than by assuming:
1. **Do the live... | ClementJ18/pySAGE | examples/sage_live/survey.py | .py | bac2c320d85520e2 | 7.35 | 4 |
"""Research an upgrade and *verify it landed* - the two scopes, and the field that lies.
Upgrades are the first thing in the live API where the obvious reading is wrong, so this example
is built around the mistake rather than around the happy path:
player.upgrades faction-wide researches, and only th... | ClementJ18/pySAGE | examples/sage_live/upgrades.py | .py | 7e6bfaaf4c8e5d66 | 7.35 | 4 |
"""Script to blend a tile with its neighbour in a given direction.
Coordinates match the world editor visual (rows top-to-bottom, columns left-to-right).
Usage:
python blend_tile.py <map_file_path> <row> <col> <direction> [output_map_path]
Directions:
right Blend towards the tile at col+1
left Blend t... | ClementJ18/pySAGE | examples/sage_map/blend_tile.py | .py | 2565f94346bec0ab | 7.35 | 4 |
"""Script to detect and fix unblended tile connections.
For every adjacent pair of tiles with different textures that lacks a blend,
the smaller texture group blends over the larger one. A blend is added on
the small-group tile, pointing toward the large-group tile.
Coordinates match the world editor visual (rows to... | ClementJ18/pySAGE | examples/sage_map/fix_blends.py | .py | 8bb67bc00ab9055f | 7.35 | 4 |
"""Script to scale a map by a numeric factor (integer or float).
Usage:
python scale_map.py <map_file_path> <scale> [output_map_path] [--scale-objects]
Example:
python scale_map.py Mission.map 3
python scale_map.py Mission.map 0.5 Mission_half.map
python scale_map.py Mission.map 3 Mission_3x.map --sca... | ClementJ18/pySAGE | examples/sage_map/scale_map.py | .py | c506ecd9e338491c | 7.35 | 4 |
"""Script to replace textures in a map file.
Usage:
1. Run the script with a map file path
2. It will generate texture_mapping.json with all unique textures
3. Edit the JSON file to specify replacements
4. Press Enter to apply the changes
Mapping file format (per texture entry):
"OldName": {"name": "NewName", "cell... | ClementJ18/pySAGE | examples/sage_map/texture_replacer.py | .py | fe442a0d02217649 | 7.35 | 4 |
"""Batch round-trip validator for `.apt`/`.const` pairs, shared by the `sage-apt
check` CLI and the corpus acceptance gate.
Each pair is decompiled, recompiled, and re-decompiled in a temporary directory so
the inputs are never touched. A pair is `ok` when the second XML matches the first,
`unstable` when they differ,... | ClementJ18/pySAGE | sage_apt/check.py | .py | b06dbdcea3385c82 | 7.35 | 4 |
"""Flag bit-field conversions for PlaceObject, Button, and ButtonAction flags."""
def _split_flags(flagstr: str) -> list[str]:
return [f.strip().lower() for f in flagstr.split("|") if f.strip()]
# PlaceObject flags
_PO_BITS = {
"move": 0x01,
"hascharacter": 0x02,
"hasmatrix": 0x04,
"hascolortran... | ClementJ18/pySAGE | sage_apt/flags.py | .py | dcaaa08787292b07 | 7.35 | 4 |
"""Parser for the APT image-map `.dat` file (`AptToBigc` output).
The `.dat` sitting beside a `.apt`/`.const` pair maps each `image` character to the
texture it samples and the sub-rectangle it crops out of that texture's atlas. Two record
kinds, one per line (`;`-prefixed lines are comments):
<imageId>-><texture... | ClementJ18/pySAGE | sage_apt/imagemap.py | .py | ff8bd5aa29905906 | 7.35 | 4 |
"""Resolve an APT `image` character to real artwork (a cropped PNG data-URI).
Ties the stdlib `.dat` image map (`sage_apt.imagemap`) to the shared texture decoder
(`sage_utils.textures.TextureSource`): an image samples texture `apt_<Movie>_<id>` and
crops the rectangle the `.dat` records out of it. Everything here nee... | ClementJ18/pySAGE | sage_apt/textures.py | .py | 83fa14e0c8643be2 | 7.35 | 4 |
"""Binary reader/writer for the SAGE engine's `asset.dat` file: the BFME2/RotWK asset cache
index of every source art file (`.w3d`/`.tga`/...), the individual assets each one provides
with their byte range inside it, and a dependency table of which assets reference which
other assets. See README.md for the full on-disk... | ClementJ18/pySAGE | sage_asset/assetdat.py | .py | 6beacce69b230d7b | 7.35 | 4 |
# Copyright 2019-present MongoDB, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | mongodb-labs/pymongoexplain | test/test_crud_v2.py | .py | 55660b145aa850c4 | 7.85 | 4 |
# Copyright 2009-2015 MongoDB, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | mongodb-labs/pymongoexplain | test/version.py | .py | 6f9fc1463d1fa6da | 7.85 | 4 |
from __future__ import annotations
import logging
import os
from collections.abc import Iterator
from itertools import chain
from typing import Any
from typing import Literal
try:
import pandas as pd
except ImportError:
pd = None # type: ignore
import pyarrow as pa
import parquery.aggregate_duckdb as aggre... | visualfabriq/parquery | parquery/aggregate.py | .py | bb6fca924c15da3c | 7.15 | 1 |
from __future__ import annotations
import gc
import logging
import os
import shutil
import uuid
from collections.abc import Iterator
from typing import Any
try:
import duckdb
HAS_DUCKDB = True
except ImportError:
HAS_DUCKDB = False
import pyarrow as pa
from parquery.tool import DataFilter
logger = log... | visualfabriq/parquery | parquery/aggregate_duckdb.py | .py | f6e6d240d6917b82 | 7.15 | 1 |
from __future__ import annotations
import gc
import logging
import pyarrow as pa
import pyarrow.compute as pc
import pyarrow.dataset as ds
# Import shared types and utilities from main aggregate module
from parquery.tool import SAFE_PREAGGREGATE, DataFilter, create_empty_result
logger = logging.getLogger(__name__)
... | visualfabriq/parquery | parquery/aggregate_pyarrow.py | .py | afb73aa46c6b30a3 | 7.15 | 1 |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
import pyarrow as pa
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
import pandas as pd
import polars as pl
try:
import duckdb # noqa: F401
HAS_DUCKDB = True
except ImportError:
HAS_DUCKDB... | visualfabriq/parquery | parquery/tool.py | .py | 54c6b2c11d27013d | 7.15 | 1 |
from __future__ import annotations
import gc
import logging
import os
import pathlib
from typing import TYPE_CHECKING
import pyarrow as pa
import pyarrow.parquet as pq
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
import pandas as pd
import polars as pl
try:
import pandas as pd
HAS_PANDAS... | visualfabriq/parquery | parquery/write.py | .py | 91d5298a11e3a895 | 7.15 | 1 |
import tempfile
import pyarrow.parquet as pq
import pytest
from parquery import aggregate_pq, df_to_parquet
try:
import polars as pl
HAS_POLARS = True
except ImportError:
HAS_POLARS = False
@pytest.mark.skipif(not HAS_POLARS, reason="polars not installed")
def test_polars_write_and_read():
"""Test... | visualfabriq/parquery | tests/test_polars.py | .py | 3bba8ba2cb48b27f | 7.65 | 1 |
import pyarrow as pa
from parquery.transport import (
deserialize_pa_table_base64,
deserialize_pa_table_bytes,
open_pa_table_stream,
serialize_pa_table_base64,
serialize_pa_table_bytes,
)
def test_pa_serialization_bytes():
"""Test PyArrow table serialization to bytes."""
# Create data dir... | visualfabriq/parquery | tests/test_serialization.py | .py | e72305ad4df1783e | 7.65 | 1 |
# SPDX-FileCopyrightText: 2019-present Snoonet
#
# SPDX-License-Identifier: MIT
"""CLI for static-site-deployer."""
import re
import sys
import tarfile
from pathlib import Path
from shutil import rmtree
from typing import Annotated
import click
import requests
import typer
from github import Github
from github.GitRe... | snoonetIRC/static-site-deployer | src/static_site_deployer/cli/__init__.py | .py | e5bbbe8afd2f5abc | 7 | 0 |
import json
import jwt
import time
import base64
import os
from app.coursegrab.utils.constants import ALGORITHM, ANDROID, EMAIL, IOS, COURSEGRAB_FROM_EMAIL, COURSEGRAB_TO_EMAIL, MAX_BCC_SIZE
from datetime import datetime
from hyper import HTTP20Connection
from firebase_admin import initialize_app, messaging
import boto... | cuappdev/coursegrab-backend | src/app/coursegrab/notifications/push_notifications.py | .py | 023972ca61fc82df | 7.15 | 1 |
from __future__ import with_statement
import logging
from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
from alembic import context
# add your model's MetaData object here
# for 'autogenerate' support
# from myapp import mymodel
# target_metadata = mymodel.Ba... | cuappdev/coursegrab-backend | src/app/migrations/env.py | .py | 9145beb53fd19295 | 7.15 | 1 |
"""Change user sessions
Revision ID: 110f14cc6f39
Revises: 320142725e82
Create Date: 2021-01-21 13:32:16.209987
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "110f14cc6f39"
down_revision = "320142725e82"
branch_labels = None
depends_on = None
def upgrade():... | cuappdev/coursegrab-backend | src/app/migrations/versions/110f14cc6f39_change_user_sessions.py | .py | 3520759989fbf3ac | 7.15 | 1 |
"""Add semesters
Revision ID: 320142725e82
Revises: c8591a7abce5
Create Date: 2020-04-21 01:49:07.098188
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "320142725e82"
down_revision = "c8591a7abce5"
branch_labels = None
depends_on = None
def upgrade():
# ... | cuappdev/coursegrab-backend | src/app/migrations/versions/320142725e82_add_semesters.py | .py | 85ef910a8e60b765 | 7.15 | 1 |
"""Update session
Revision ID: 3cfb8cf58e98
Revises: 110f14cc6f39
Create Date: 2021-04-04 01:26:03.892507
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '3cfb8cf58e98'
down_revision = '110f14cc6f39'
branch_labels = None
depends_on = None
def upgrade():
#... | cuappdev/coursegrab-backend | src/app/migrations/versions/3cfb8cf58e98_update_session.py | .py | d30f3b1f00f4633d | 7.15 | 1 |
"""Initial migration
Revision ID: 6ce1a6f069ff
Revises:
Create Date: 2020-03-04 17:23:44.459614
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "6ce1a6f069ff"
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto g... | cuappdev/coursegrab-backend | src/app/migrations/versions/6ce1a6f069ff_initial_migration.py | .py | 7cf88d14b39ddeab | 7.15 | 1 |
"""Add professors
Revision ID: 8bee00207b63
Revises: 6ce1a6f069ff
Create Date: 2020-03-08 21:48:22.794206
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "8bee00207b63"
down_revision = "6ce1a6f069ff"
branch_labels = None
depends_on = None
def upgrade():
#... | cuappdev/coursegrab-backend | src/app/migrations/versions/8bee00207b63_add_professors.py | .py | db26dcd31e01ecb7 | 7.15 | 1 |
"""Add notifications
Revision ID: c8591a7abce5
Revises: 8bee00207b63
Create Date: 2020-03-11 18:08:51.709412
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "c8591a7abce5"
down_revision = "8bee00207b63"
branch_labels = None
depends_on = None
def upgrade():
... | cuappdev/coursegrab-backend | src/app/migrations/versions/c8591a7abce5_add_notifications.py | .py | 0ca2edda6c744357 | 7.15 | 1 |
"""
Git host client factory module.
The client factory uses a dispatcher pattern to ease registration of new git
host APIs.
To register a new git host API implementation to this factory, simply decorate
the client class with @api_client(api_name).
Example:
# bert_e.git_host.bitbucket module
from bert_e.git... | scality/bert-e | bert_e/git_host/factory.py | .py | db6b7b83784f81ed | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/job.py | .py | a1ff786a1c12fa94 | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/jobs/delete_queues.py | .py | 197b4426e7b627ed | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/lib/dispatcher.py | .py | 4cf39285f58765fe | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/lib/lru_cache.py | .py | 56a53def2d296c5c | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/lib/retry.py | .py | 33a6576af5082bcd | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/lib/settings_dict.py | .py | 4626fd8cafd9b312 | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/lib/simplecmd.py | .py | ae9ed08f88eda369 | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/server/__init__.py | .py | 5ab5a8dac49df127 | 7.15 | 1 |
# Copyright 2016-2018 Scality
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | scality/bert-e | bert_e/server/api/__init__.py | .py | de46afc6db6e65bb | 7.15 | 1 |
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