Clone the repo: git clone https://github.com/mwkyuen/hotel.git
Enter the repo directory: cd hotel/
Set up and launch your virtual environment (optional). Instructions can be found here
Install the Hotel Concierge library by running: pip install --editable .
The current working directory should be /path/to/hotel/
You are now READY to proceed!
The Hotel Concierge Library is a Command Line Interface (CLI) program with 13 functions. A general description of each function can be viewed using the command hotel --help. Moreover, a more detailed description of each function can be viewed by using the command hotel FUNCTION --help.
Object passed throughout the CLI program. It has six components:
- Current working directoy (/path/to/hotel)
- Client list
- Client Supplementary info
- Hotel
- Reservations dataframe
- Intervals dataframe
Not applicable in Begin, Quit, Initialize functions
In order to get started, it is necessary to first initialize a Hotel object which the library will be working with. This initialization also sets up a session.csv file. The Concierge is able to handle multiple hotels, and the session.csv file keeps track of which hotel is currently being administered. Once a session is over the command hotel quit can be used to close the Hotel Concierge. Before new commands can be accepted, the command hotel begin /path/to/session.csv will need to be called, pointing to the hotel session to be administered.
A hotel can be initialized by calling hotel initialize *.json. The *.json file must be present in the same directory as the current working directory. You can use a custom *.json file or you can use the ones supplied in the samples/ folder, simply copy over one of the *.json files from the samples/ folder into your current working directory.
If using a custom *.json file, the *.json file must meet the minimal schema described below:
schema = { 'type': 'object',
'properties': {
'hotel_name': {
'type': 'string',
'description': 'Name of hotel'
},
'rooms': {
'type': 'array',
'items': {'$ref': '#/definitions/room'}
}
},
'definitions': {
'room': {
'type': 'object',
'required': ['number', 'type', 'cost'],
'properties': {
'number': {
'type': 'integer',
'description': 'Room number'
},
'type': {
'type': 'integer',
'description': 'Room type'
},
'cost': {
'type': 'integer',
'description': 'Cost of room per night'
}
}
}
},
'required': ['hotel_name', 'rooms']
}
The schema dictates that the hotels have at least a name and an array of rooms. However, additional information can be added (as seen in the samples/*.json) which can be utilized in functions under future developments.
The Initialize command creates a new data/hotel_{name} directory which will hold all the information of the hotel state. Inbetween sessions, the session.csv file will also be stored under this directory.
The clients of each hotels are stored in the client_list.csv as a client_list object, and the client_supp.csv files as a client_supp object. Each client is assigned a unique client ID which is used to index additional information about the clients. Each client can exist in three possible states.
- State 1 is a client who is currently checked-in at the hotel.
- State 2 is a client with an existing reservation at the hotel.
- State 3 is a client who has neither a reservation nor currently checked-in (default state)
As commands are issued to the Concierge the client objects and the relevant *.csv files are updated.
Room of the hotel are represented with Reservations and Intervals objects.
Each room has a list of incoming reservations, which will be removed once a client has either checked-in or decided to cancel the reservation. The Concierge holds the Reservations object as a sorted pandas Dataframe with DatetimeIndex (start), rooms as columns names, and client ID as values. Values are sorted by DatetimeIndex (i.e the next reservation for Room X will be at the top)
Each room also has a list of available intervals, to which a new reservation can be added to, splicing the interval into two remaining interval segments. The Concierge holds the Intervals object as a sorted pandas Dataframe with DatetimeIndex (start), rooms as column names, and Datatime (end) as values.
After a while you might find that reserve-dates() runs slow because of increasing expired intervals. Run hotel clear-cache to speed up the process.
As commands are issued to the Concierge the room objects and the relevant *.csv files are updated.
First setup the Hotel Concierge library as detailed in Setup above.
There are sample hotels located in samples/hotel_*.json. Hotel A has three rooms, all of Type 1. Hotel B has six rooms, Rooms 1 & 2 of Type 1, Rooms 3 & 4 of Type 2, and Rooms 5 & 6 of Type 3
Room types represent the luxury and amenities provided to each room
cp samples/hotel_B.json ./ #Copy a sample hotel to your hotel directory
hotel initialize hotel_B.json #Initialize, creating new directories and starting new session for Hotel B
hotel register t.king tiger.king@gmail.com #Register new client
hotel get-client-id t.king tiger.king@gmail.com #Check client ID
hotel reserve-dates 0 3 2020-08-22 2020-08-28 #Reserve dates for Client_0, room Type_3
hotel quit #Close current session
hotel begin data/hotel_B/session.csv #Open session for Hotel B
hotel register s.beast sexy.beast@gmail.com #Register new client
hotel register a.possum awesome.possum@gmail.com #Register new client
hotel get-client-id s.beast sexy.beast@gmail.com #Check client ID
hotel get-client-id a.possum awesome.possum@gmail.com #Check client ID
hotel reserve-dates 1 2 2020-09-14 2020-09-18 #Reserve dates for Client_1, room Type_2
hotel reserve-dates 2 3 2020-08-22 2020-08-28 #Reserve dates for Client_2, room Type_3
hotel get-all-clients #Print all clients
hotel delete-reservation 2 #Delete reservation for Client_2
hotel get-some-clients 2 #Print clients which are in State_3
hotel get-one-client 2 #Print the details of Client_2
hotel get-one-client 1 #Print the details of Client_1
hotel check-in 0 #Check in for Client_0
hotel check-out 0 yes #Check out for Clent_0, boolean if they have paid
hotel quit #Close current session
Currently all files are written into *.csv files. Objects are seperated across multiple files (i.e Rooms object), and files were designed to accommodate manual inputs and easy viewing. However, this approach is prone to mistakes and corruption of data files.
Moving forward, I plan to use the sqlite3 python library to handle the hotel database information in a single .db file and disallow manual inputs.
I also plan to add additional functions which will be helpful to the Concierge in performing its duties. If you have suggestions of additional function you would like to see, let me know and I will try to accommadate them. Thank you for reading these docs.