flowchart TB
c1-->a2
subgraph ide1 [one]
a1-->a2
end
graph RL
database("Database") <-."network"..-> DAO_Implementation
DAO_Implementation <--> DAOinterface("DAO Interface")
DAOinterface <--> service("Servive / service interface")
service <--> controller("Controller")
controller <-."network"..-> client("Client")
subgraph DAO_Implementation
dataSource
entityManager("Entity Manager")
AUTHOR: Shawn Armstrong
CONTACT: smarmstr@ucsc.edu
- This guide serves as a comprehensive reference for essential knowledge required to begin developing plugins for OpenTAP.
As a university student majoring in Computer Science, I have spent the last six months participating in a project focused on creating an OpenTAP plugin. Although the technical details are well-covered in OpenTAP's official documentation, it lacks some high-level aspects that are essential for beginner developers to grasp the whole picture. This document aims to bridge such knowledge gaps for newcomers.
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from __future__ import print_function | |
from googleapiclient.discovery import build | |
from google.oauth2 import service_account | |
from selenium import webdriver | |
import time | |
SERVICE_ACCOUNT_FILE = 'keys.json' | |
SCOPES = ['https://www.googleapis.com/auth/spreadsheets'] | |
creds = None |
- This file explores using Python's unit testing framework, PyTest, in conjunction with OpenTAP's Python resources.
- OpenTAP was built using the .NET framework; Python capabilities were later added via the Python.NET library.
- Python.NET is a bridge between Python and .NET platforms, enabling interoperation between the two. This tool allows Python programs to access the .NET Framework and Mono libraries; similarly, it provides .NET applications the ability to utilize Python libraries.
- This demonstration is using Windows 11
- This demonstration is using Python 3.11.3
- This document contains a detailed review and analysis of the binary search algorithm, using an example implemented in Haskell.
- Binary search is an algorithm used to search for a specific value in a sorted list, as it relies on the property that each step can exclude half of the remaining elements.
- Given a list
$L$ and a target value$T$ the algorithm will do the following:
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# data.CSV must be in the same directory as this script. | |
# Numpy, Pandas and mysql-connector-python are required. | |
import pandas as pd | |
import mysql.connector | |
from mysql.connector import Error | |
import re | |
def sanitize_column_name(column): | |
sanitized_column = re.sub(r'\W+', '_', column).strip('_') |
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