Freecodecamp Scientific Computing with Python Certification; Solution for Challenge 2: Time Calculator

Overview

Assignment

Write a function named add_time that takes in two required parameters and one optional parameter:

  • a start time in the 12-hour clock format (ending in AM or PM)
  • a duration time that indicates the number of hours and minutes
  • (optional) a starting day of the week, case insensitive

The function should add the duration time to the start time and return the result.

If the result will be the next day, it should show (next day) after the time. If the result will be more than one day later, it should show (n days later) after the time, where "n" is the number of days later.

If the function is given the optional starting day of the week parameter, then the output should display the day of the week of the result. The day of the week in the output should appear after the time and before the number of days later.

Below are some examples of different cases the function should handle. Pay close attention to the spacing and punctuation of the results.

add_time("3:00 PM", "3:10")
# Returns: 6:10 PM

add_time("11:30 AM", "2:32", "Monday")
# Returns: 2:02 PM, Monday

add_time("11:43 AM", "00:20")
# Returns: 12:03 PM

add_time("10:10 PM", "3:30")
# Returns: 1:40 AM (next day)

add_time("11:43 PM", "24:20", "tueSday")
# Returns: 12:03 AM, Thursday (2 days later)

add_time("6:30 PM", "205:12")
# Returns: 7:42 AM (9 days later)

Do not import any Python libraries. Assume that the start times are valid times. The minutes in the duration time will be a whole number less than 60, but the hour can be any whole number.

Development

Write your code in time_calculator.py. For development, you can use main.py to test your time_calculator() function. Click the "run" button and main.py will run.

Testing

The unit tests for this project are in test_module.py. We imported the tests from test_module.py to main.py for your convenience. The tests will run automatically whenever you hit the "run" button.

Submitting

Copy your project's URL and submit it to freeCodeCamp.

Owner
Hellen Namulinda
JavaScript/Python Developer
Hellen Namulinda
Irrigation controller for Home Assistant

Irrigation Unlimited This integration is for irrigation systems large and small. It can offer some complex arrangements without large and messy script

Robert Cook 176 Jan 02, 2023
AI that generate music

PianoGPT ai that generate music try it here https://share.streamlit.io/annasajkh/pianogpt/main/main.py or here https://huggingface.co/spaces/Annas/Pia

Annas 28 Nov 27, 2022
Reinfore learning tool box, contains trpo, a3c algorithm for continous action space

RL_toolbox all the algorithm is running on pycharm IDE, or the package loss error may exist. implemented algorithm: trpo a3c a3c:for continous action

yupei.wu 44 Oct 10, 2022
ML-Ensemble – high performance ensemble learning

A Python library for high performance ensemble learning ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framew

Sebastian Flennerhag 764 Dec 31, 2022
网络协议2天集训

网络协议2天集训 抓包工具安装 Wireshark wireshark下载地址 Tcpdump CentOS yum install tcpdump -y Ubuntu apt-get install tcpdump -y k8s抓包测试环境 查看虚拟网卡veth pair 查看

120 Dec 12, 2022
A PaddlePaddle version image model zoo.

Paddle-Image-Models English | 简体中文 A PaddlePaddle version image model zoo. Install Package Install by pip: $ pip install ppim Install by wheel package

AgentMaker 131 Dec 07, 2022
coldcuts is an R package to automatically generate and plot segmentation drawings in R

coldcuts coldcuts is an R package that allows you to draw and plot automatically segmentations from 3D voxel arrays. The name is inspired by one of It

2 Sep 03, 2022
Official code repository for "Exploring Neural Models for Query-Focused Summarization"

Query-Focused Summarization Official code repository for "Exploring Neural Models for Query-Focused Summarization" This is a work in progress. Expect

Salesforce 29 Dec 18, 2022
VOS: Learning What You Don’t Know by Virtual Outlier Synthesis

VOS This is the source code accompanying the paper VOS: Learning What You Don’t

248 Dec 25, 2022
PyTorch implementation of PSPNet segmentation network

pspnet-pytorch PyTorch implementation of PSPNet segmentation network Original paper Pyramid Scene Parsing Network Details This is a slightly different

Roman Trusov 532 Dec 29, 2022
Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification"

hypergraph_reid Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification" If you find this help your research,

62 Dec 21, 2022
DISTIL: Deep dIverSified inTeractIve Learning.

DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.

decile-team 110 Dec 06, 2022
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates

Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates Installation Clone the repository: git clone https://github.com/Zengyi-Qi

Zengyi Qin 3 Oct 18, 2022
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation.

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

HYOJINPARK 22 Jan 01, 2023
Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure.

Event Queue Dialect Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure. Motivation The m

Cornell Capra 23 Dec 08, 2022
nanodet_plus,yolov5_v6.0

OAK_Detection OAK设备上适配nanodet_plus,yolov5_v6.0 Environment pytorch = 1.7.0

炼丹去了 1 Feb 18, 2022
Kaggle: Cell Instance Segmentation

Kaggle: Cell Instance Segmentation The goal of this challenge is to detect cells in microscope images. with simple view on how many cels have been ann

Jirka Borovec 9 Aug 12, 2022
U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

Xuebin Qin 6.5k Jan 09, 2023
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Knowledge Informed Machine Learning using a Weibull-based Loss Function Exploring the concept of knowledge-informed machine learning with the use of a

Tim 43 Dec 14, 2022
Multi-Task Deep Neural Networks for Natural Language Understanding

New Release We released Adversarial training for both LM pre-training/finetuning and f-divergence. Large-scale Adversarial training for LMs: ALUM code

Xiaodong 2.1k Dec 30, 2022