├── .gitignore ├── .vscode └── settings.json ├── LICENSE ├── README.md ├── images ├── MACRO_mistake.png ├── OS_mistake.png ├── class.png ├── class_condensed.png ├── compre.png ├── mids.png └── tts.png └── src ├── converter.py ├── create_json.py ├── files ├── my_timetables.json ├── output.csv ├── timetable.json └── timetable.pdf ├── parse_times.py ├── poetry.lock ├── pyproject.toml ├── timetables.py └── visualize.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # For MacOS 132 | src/.DS_Store -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "[python]": { 3 | "editor.defaultFormatter": "ms-python.black-formatter" 4 | }, 5 | "python.formatting.provider": "none", 6 | "conventionalCommits.scopes": [ 7 | "add variable values that match the example pdf" 8 | ] 9 | } 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Chronoscript 2 | 3 | Chronofactorem exists, yes. It helps you easily make your timetable while checking for possible clashes, yes. But is it the timetable that matches your need the most? Maybe, you'd never know for sure. 4 | 5 | Depending on the courses you want, and the CDCs you need to take, there can be thousands of possible timetables _(psst.. CS 2-2 (2022-23) had over **30,000** timetables)_. 6 | 7 | Going through each of them is a .. not so fun task. Well, to an extent you can now automate this. 8 | 9 | Chronoscript currently automates the process of: 10 | 11 | 1. Converting the timetable pdf to a csv file. 12 | 2. Converting the csv file to a json file. (used for the chronofactorem website and the timetables script). 13 | 3. Generating all possible timetables with the given set of courses (without class hours **or** exam timing clashes). 14 | 4. Filters them and sorts them based on your needs. (days you want free, or days you want _liter_ than others). 15 | 16 | **Note:** The first two features are mainly for future developers, and not for users. However, instructions to use those features have been provided as well. 17 | 18 | ## Important information 19 | 20 | 1. This is **not** an official BITS project. We work with the timetable pdf that is made available to all students, and if there are any errors in details/formatting of the timetable, the tool may not work as expected. So, after the timetables are formed, it is advised to verify the timetable of your choice with the official timetable pdf (including exams). 21 | 22 | 2. The scripts generate multiple possible/permutations of the timetables which do not have any clashes, it is very compute heavy. This is why it is not going to be incorporated as part of the chronofactorem website as of now, and can only be run locally. 23 | 24 | 3. Additionally, it was not built with first-years in mind as your timetables will not have clashes and there are too many to take care of. However we will be working on optimising the code to make it work for first-years as well in the future. 25 | 26 | ## Contributing 27 | 28 | 1. We accept feature requests and bug reports as issues on this repository. If you want to contribute code, please open a pull request. 29 | 30 | 2. We appreciate any kind of help, be it code, documentation, or even just suggestions. If you want to contribute but don't know where to start, please reach out to any one of us. 31 | 32 | ## System requirements 33 | 34 | 1. Python `3.7+`, `3.10.11` is recommended. (Download from [here](https://www.python.org/downloads/), if you don't already have it installed) 35 | 36 | 2. Poetry (package manager for python) `1.5.0` is recommended. (Installation instructions for all platforms [here](https://python-poetry.org/docs/#installation)) 37 | 38 | Quick installation instructions For Linux/MacOS/WSL, run the following command in your terminal: 39 | 40 | ```bash 41 | 42 | curl -sSL https://install.python-poetry.org | python3 - 43 | 44 | ``` 45 | 46 | **Note:** Make sure you add poetry to your path variable as well. 47 | 48 | [**Common issue in adding poetry to path in MacOS**](https://github.com/python-poetry/poetry/issues/507) 49 | 50 | 3. **Optional**: If you wish to create you timetable form the PDF directly, you will also need the timetable pdf as given by the administration. 51 | 52 | **Note:** Using the updated CSV/JSON file provided in the repository is recommended, as at least some, if not all errors have already been removed. If you wish to create your own CSV/JSON file, follow the instructions in the [Generating the CSV file](#generating-the-csv-and-json-file) section. 53 | 54 | ## Usage instructions 55 | 56 | 1. Clone this repository to your local machine (or) download the zip file and extract it. 57 | 58 | 2. `cd` to the src directory. 59 | 60 | 3. Run `poetry shell` to activate the virtual environment. 61 | 62 | 4. Run `poetry install` to install all the dependencies. 63 | 64 | 5. If there are any particular course sections you'd like to avoid, remove them from the [json](./src/timetable.json) file. 65 | 66 | **Note:** We are currently working on a feature that allows you to do this interactively, but for now you'll have to do it manually. 67 | 68 | 6. Open `timetables.py` and navigate to the bottom of the file. 69 | 70 | 7. Modify the `CDCs`, `DELs`, `OPELs` and `HUELs` variables with the course codes you want (exhaustive) in your timetable. 71 | 72 | 8. Choose the number of DELs, OPELs and HUELs you want from these in `nDels`, `nOpels` and `nHuels` respectively. Leave as `len(...)`, if you want to pick all of them for sure. 73 | 74 | 9. Modify the `free_days` variable with the days you want to have free if possible. 75 | 76 | 10. Modify the `lite_order` variable with the relative order of _liteness_ you want to have in your timetable. For example, if my order is 77 | 78 | ```python 79 | ["S", "M", "Su", "F", "T", "W", "Th"] 80 | ``` 81 | 82 | , then I want to have a timetable with the least number of hours on Saturday, then Monday, then Sunday, and so on. 83 | 84 | 11. Run `poetry run python timetables.py` to generate the timetables. 85 | 86 | The console output will show you the rough results of your filters, and the number of timetables generated. 87 | 88 | It additionally prints out the timetable that most suits your needs, and the one that matches your minimum requirements but is the furthest from your ideal timetable. 89 | 90 | Format: `(number of free days matched, [number of hours of classes per day, in order of the lite_order variable], timetable)` 91 | 92 | **Note**: This is used only for testing purposes and those who are comfortable with the script. You can visualize them as tables in the next step. 93 | 94 | ## Further instructions 95 | 96 | 1. You will now have a `my_timetables.json` file created. 97 | 98 | 2. This can be be visualized as tables using the `visualize.py` script, by specifying the index of the timetable you want to visualize. (`0`: most ideal, `n-1` or `-1`: least ideal) (where `n` denotes number of timetables generated) 99 | 100 | 3. Run `poetry run python visualize.py` to visualize the timetable. 101 | 102 | ### Example outputs 103 | 104 | #### Class schedule: condensed 105 | 106 | ![class sched](images/class_condensed.png) 107 | 108 | #### Class schedule: expanded 109 | 110 | ![class sched](images/class.png) 111 | 112 | #### Midsem schedule 113 | 114 | ![mids sched](images/mids.png) 115 | 116 | #### Compre schedule 117 | 118 | ![compre sched](images/compre.png) 119 | 120 | #### Matching your ideal timetable 121 | 122 | ![ideal](images/tts.png) 123 | 124 | ## Generating the CSV and JSON file 125 | 126 | 5. Open `converter.py` and navigate to the bottom of the file. 127 | 128 | 6. Modify the variables as the comments suggest to suit your needs/timetable. `(headers, file and page_range)` 129 | 130 | 7. Run `poetry run python converter.py` to generate the csv file. 131 | 132 | 8. Fix any errors in formatting or course details in the CSV file (see more below in the [Fixing errors in the CSV file](#fixing-errors-in-the-csv-file) section). 133 | 134 | 9. Open `create_json.py` and navigate to the bottom of the file. 135 | 136 | 10. In the general case, there shouldn't be any need to modify the variables. However, if there is a column reorder in the csv file, you can modify the `columns` variable to match the order of the columns in the csv file. 137 | 138 | 11. Run `poetry run python create_json.py` to generate the json file. 139 | 140 | ## Fixing errors in the CSV file 141 | 142 | In some cases there can be formatting errors/course detail errors are present in the PDF given by the administration. In such cases, the CSV file generated will have errors. We will need to fix these before creating our `timetable.json` file. 143 | 144 | Examples of such errors are: 145 | 146 | 1. Formatting errors: 147 | 148 | ![OS](images/OS_mistake.png) 149 | 150 | Here, the `hours` column should ideally just have `13` like the other courses, hence we need to fix this in the generated csv file from `"13(8.00p\nm)"` to `"13"`. 151 | 152 | 2. Course detail errors: 153 | 154 | ![MACRO](images/MACRO_mistake.png) 155 | 156 | Here, the word `Tutorial` should be in the row below, and the corresponding section should be marked as `1` (latter fix is optional, as script takes care of it). So once again, we need to fix this in the generated csv file. 157 | 158 | ## Features we are working on (in no particular order) 159 | 160 | 1. Generating a CLI/TUI that works across all platforms that will let you do this interactively, and not having to modify and src files. 161 | 162 | 2. Functionality to balance out the timetables across the week, so that you don't have too many classes on one day and too few on another. 163 | 164 | 3. Functionality to sort timetables by exam liteness. (and set priority to exam liteness over class liteness, or vice versa) 165 | 166 | 4. (Tentative) Functionality to add any course under a department that follows your filters to satisfy a course requirement. 167 | 168 | 5. (Tentative) Working with first-year timetables. 169 | -------------------------------------------------------------------------------- /images/MACRO_mistake.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/MACRO_mistake.png -------------------------------------------------------------------------------- /images/OS_mistake.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/OS_mistake.png -------------------------------------------------------------------------------- /images/class.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/class.png -------------------------------------------------------------------------------- /images/class_condensed.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/class_condensed.png -------------------------------------------------------------------------------- /images/compre.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/compre.png -------------------------------------------------------------------------------- /images/mids.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/mids.png -------------------------------------------------------------------------------- /images/tts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/images/tts.png -------------------------------------------------------------------------------- /src/converter.py: -------------------------------------------------------------------------------- 1 | import pdfplumber 2 | import pandas as pd 3 | 4 | 5 | def remove_headers( 6 | table: list[list[str]], remove_rows_containing_any_of: list[str] 7 | ) -> list[list[str]]: 8 | """ 9 | Function to remove headers from the table. I.e. remove rows that contain any of the strings in the list which do not contribute to the content of the timetable. 10 | 11 | Args: 12 | table (list[list[str]]): The table to remove the headers from. 13 | remove_rows_containing_any_of (list[str]): The list of strings to check for in the table, and remove the row if any of the strings are found. 14 | 15 | Returns: 16 | list[list[str]]: The table with the headers removed. 17 | """ 18 | new_table: list[list[str]] = [] 19 | for row in table: 20 | transfer: bool = True 21 | for string in remove_rows_containing_any_of: 22 | if string in row: 23 | transfer: bool = False 24 | break 25 | if transfer: 26 | new_table.append(row) 27 | return new_table 28 | 29 | 30 | def convert_timetable_to_csv( 31 | pages: list[pdfplumber.page.Page], headers: list[str] 32 | ) -> pd.DataFrame(): 33 | """ 34 | Function to convert the timetable to a pandas dataframe. 35 | 36 | Args: 37 | pages (list[pdfplumber.page.Page]): The pages to extract the timetable from. 38 | headers (list[str]): The headers to remove from the table. 39 | 40 | Returns: 41 | pd.DataFrame(): The timetable as a pandas dataframe. 42 | """ 43 | df = pd.DataFrame() 44 | for page in pages: 45 | table = page.extract_table() 46 | table = remove_headers( 47 | table, 48 | headers, 49 | ) 50 | df = pd.concat([df, pd.DataFrame(table)]) 51 | return df 52 | 53 | 54 | if __name__ == "__main__": 55 | # headers to remove from the table 56 | headers: list[str] = ["COMP\nCODE", "DRAFT TIMETABLE II SEMESTER 2024 -25"] 57 | 58 | # page range to extract the timetable from 59 | # [from, to] 60 | page_range: list[int] = [1, 66] 61 | 62 | # path to the pdf file 63 | file: str = r"./files/timetable.pdf" 64 | 65 | pdf: pdfplumber.pdf.PDF = pdfplumber.open(file) 66 | 67 | # might need to play around with the +-1, depending on how the pdf is formatted and how pdfplumber extracts the pages 68 | pages: list[pdfplumber.page.Page] = pdf.pages[page_range[0] - 1 : page_range[1]] 69 | 70 | data: pd.DataFrame = convert_timetable_to_csv(pages, headers) 71 | 72 | # output the dataframe to csv 73 | data.to_csv("./files/output.csv", index=False) 74 | -------------------------------------------------------------------------------- /src/create_json.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import json 4 | from parse_times import parse_time, parse_compre_time 5 | 6 | 7 | def isnan(value): 8 | """ 9 | Function to check if a value is NaN. 10 | 11 | Args: 12 | value: The value to check. 13 | 14 | Returns: 15 | bool: True if the value is NaN, False otherwise. 16 | """ 17 | try: 18 | import math 19 | 20 | return math.isnan(float(value)) 21 | except: 22 | return False 23 | 24 | 25 | def convert_all_sets_to_list_recursive(obj: dict) -> dict: 26 | """ 27 | Function to convert all sets in a dictionary to lists recursively. This is done because sets are not serializable. 28 | 29 | Args: 30 | obj (dict): The dictionary to convert the sets in. 31 | 32 | Returns: 33 | dict: The dictionary with all sets converted to lists. 34 | """ 35 | if isinstance(obj, dict): 36 | for key, value in obj.items(): 37 | obj[key] = convert_all_sets_to_list_recursive(value) 38 | elif isinstance(obj, set): 39 | obj = list(obj) 40 | return obj 41 | 42 | 43 | def remove_duplicate_dicts(l: list[dict]) -> list[dict]: 44 | """ 45 | Function to remove duplicate dictionaries from a list of dictionaries. 46 | 47 | Args: 48 | l (list[dict]): The list of dictionaries to remove duplicates from. 49 | 50 | Returns: 51 | list[dict]: The list of dictionaries with duplicates removed. 52 | """ 53 | seen = set() 54 | new_l = [] 55 | for d in l: 56 | t = tuple(sorted(d.items())) 57 | if t not in seen: 58 | seen.add(t) 59 | new_l.append(d) 60 | 61 | return new_l 62 | 63 | 64 | def null_empty_exams(course_json: dict) -> dict: 65 | """ 66 | Function to nullify empty exams in a course json. 67 | 68 | Args: 69 | course_json (dict): The course json to nullify empty exams in. 70 | 71 | Returns: 72 | dict: The course json with empty exams nullified. 73 | """ 74 | for course_code in course_json["courses"]: 75 | course = course_json["courses"][course_code] 76 | if len(course.get("exams")) == 0: 77 | course["exams"] = [{}] 78 | if len(course.get("exams_iso")) == 0: 79 | course["exams_iso"] = [{}] 80 | if course["exams"][0].get("midsem") == None: 81 | course["exams"][0]["midsem"] = None 82 | if course["exams"][0].get("compre") == None: 83 | course["exams"][0]["compre"] = None 84 | if course["exams_iso"][0].get("midsem") == None: 85 | course["exams_iso"][0]["midsem"] = None 86 | if course["exams_iso"][0].get("compre") == None: 87 | course["exams_iso"][0]["compre"] = None 88 | 89 | return course_json 90 | 91 | 92 | def create_json_file( 93 | timetable: pd.DataFrame, 94 | columns: list[str], 95 | output_file: str, 96 | year: int, 97 | academic_year: int, 98 | semester: int, 99 | ) -> None: 100 | """ 101 | Function to create a json file from a timetable dataframe. 102 | 103 | Args: 104 | timetable (pd.DataFrame): The timetable dataframe to create the json file from. 105 | columns (list[str]): The columns of the dataframe. 106 | output_file (str): The name of the output json file. 107 | year (int): The academic year of the timetable. (example: 2023) 108 | academic_year (int): The academic year of the timetable. (example: 2021 is for acad year 2021-2022) 109 | semester (int): The semester of the timetable. (example: 1 is for odd semester, 2 is for even semester) 110 | """ 111 | tt: pd.DataFrame = timetable 112 | course_json: dict = {} 113 | tt.columns = columns 114 | tt.drop(columns=["serial"], inplace=True) 115 | 116 | # Filling all empty rows with the previous row's value for simplicity 117 | # tt.fillna(method="ffill", inplace=True) 118 | 119 | cols = [ 120 | "course_code", 121 | "course_name", 122 | "section", 123 | "instructor", 124 | ] 125 | tt.loc[:, cols] = tt.loc[:, cols].ffill() 126 | 127 | # fix for 2024-2025 sem 1 draft timetable 128 | # tt["compre"] = tt["midsem"] + " " + tt["compre"] 129 | # tt = tt.assign(midsem="NaN") 130 | # tt = tt[tt["course_code"] != "COURSE NO."] 131 | # tt = tt[tt["course_code"].notna()] 132 | 133 | # note that this automates the boring stuff, it does not automate the truly fascinating spliced fuckups 134 | # f = -1 135 | # for i, row in tt.iterrows(): 136 | # if f == -1 or row["course_name"] in ["Lecture", "Tutorial", "Practical"] or prev_row["course_name"] in ["Lecture", "Tutorial", "Practical"]: 137 | # prev_row = row 138 | # f = 1 139 | # continue 140 | # if row["course_code"] == prev_row["course_code"] and row["course_name"].strip() != prev_row["course_name"].strip(): 141 | # print(prev_row["course_name"], row["course_name"]) 142 | # prev_row = row 143 | 144 | for _, row in tt.iterrows(): 145 | course_code = row["course_code"] 146 | 147 | # initialize course and course details if not already initialized 148 | if course_json.get(course_code) is None: 149 | course_json[course_code] = {} 150 | course_json[course_code]["units"] = row["U"] 151 | course_json[course_code]["lecture units"] = row["L"] 152 | course_json[course_code]["practical units"] = row["P"] 153 | if course_json[course_code].get("course_name") is None: 154 | course_json[course_code]["course_name"] = row["course_name"] 155 | if course_json[course_code].get("sections") is None: 156 | course_json[course_code]["sections"] = {} 157 | 158 | # make sure sections of different kinds of classes (lecture, tutorial, practical) are not overwritten by each other 159 | # to make sure of that, and for common lingo, we add a prefix to the section number 160 | 161 | # section = int(row["section"]) 162 | # if row["course_name"] == "Tutorial": 163 | # section = "T" + str(section) 164 | # elif (row["course_name"] == "Practical") or ( 165 | # course_json[course_code]["lecture units"] == "-" 166 | # and course_json[course_code]["practical units"] != "-" 167 | # ): 168 | # section = "P" + str(section) 169 | # else: 170 | # section = "L" + str(section) 171 | 172 | # New 2024-25 Draft timetable for sem 2 already has the above part formatted 173 | section = row["section"] 174 | 175 | # initialize section and section details if not already initialized 176 | if course_json[course_code]["sections"].get(section) is None: 177 | course_json[course_code]["sections"][section] = {} 178 | 179 | if course_json[course_code]["sections"][section].get("instructor") is None: 180 | course_json[course_code]["sections"][section]["instructor"] = set() 181 | 182 | # add instructor to the set of instructors for the section 183 | course_json[course_code]["sections"][section]["instructor"].add( 184 | row["instructor"] 185 | ) 186 | 187 | # initialize schedule if not already initialized 188 | if course_json[course_code]["sections"][section].get("schedule") is None: 189 | course_json[course_code]["sections"][section]["schedule"] = [] 190 | 191 | # add schedule to the list of schedules for the section 192 | # list of schedules is a list of dictionaries, where each dictionary is a schedule 193 | # we kept it as a list, as a class may have multiple schedules (eg: "T Th @ 4" and "S @ 2") 194 | dictionary = {} 195 | if not isnan(row["room"]): 196 | dictionary["room"] = row["room"] 197 | else: 198 | dictionary["room"] = np.nan 199 | 200 | if not isnan(row["days"]): 201 | dictionary["days"] = tuple(row["days"].split()) 202 | else: 203 | dictionary["days"] = np.nan 204 | 205 | if not isnan(row["hours"]): 206 | dictionary["hours"] = tuple([int(x) for x in list(row["hours"].split())]) 207 | else: 208 | dictionary["hours"] = np.nan 209 | 210 | if not ( 211 | isnan(dictionary["room"]) 212 | and isnan(dictionary["days"]) 213 | and isnan(dictionary["hours"]) 214 | ): 215 | course_json[course_code]["sections"][section]["schedule"].append(dictionary) 216 | 217 | # remove duplicate schedules 218 | course_json[course_code]["sections"][section][ 219 | "schedule" 220 | ] = remove_duplicate_dicts( 221 | course_json[course_code]["sections"][section]["schedule"] 222 | ) 223 | 224 | # initialize exams if not already initialized 225 | if course_json[course_code].get("exams") is None: 226 | course_json[course_code]["exams"] = [] 227 | 228 | exam_dict = {} 229 | # add exams to the list of exams for the course 230 | if not isnan(row["midsem"]): 231 | exam_dict["midsem"] = row["midsem"] 232 | 233 | if not isnan(row["compre"]): 234 | exam_dict["compre"] = row["compre"] 235 | 236 | if exam_dict: 237 | course_json[course_code]["exams"].append(exam_dict) 238 | 239 | # remove duplicate exams 240 | course_json[course_code]["exams"] = remove_duplicate_dicts( 241 | course_json[course_code]["exams"] 242 | ) 243 | 244 | # parse exam times 245 | for course_code in course_json: 246 | # remove lecture units, practical units 247 | del course_json[course_code]["lecture units"] 248 | del course_json[course_code]["practical units"] 249 | exams_list = course_json[course_code]["exams"] 250 | exams_iso = [] 251 | for exam in exams_list: 252 | exam_iso = {} 253 | 254 | if exam.get("midsem"): 255 | exam_iso["midsem"] = parse_time(exam["midsem"], year) 256 | 257 | if exam.get("compre"): 258 | exam_iso["compre"] = parse_compre_time(exam["compre"], year) 259 | 260 | if exam_iso: 261 | exams_iso.append(exam_iso) 262 | # remove duplicate exams 263 | course_json[course_code]["exams_iso"] = remove_duplicate_dicts(exams_iso) 264 | 265 | # convert file to serializable format 266 | convert_all_sets_to_list_recursive(course_json) 267 | final_json = {} 268 | final_json["metadata"] = { 269 | "acadYear": academic_year, 270 | "semester": semester, 271 | } 272 | final_json["courses"] = course_json 273 | 274 | final_json = null_empty_exams(final_json) 275 | 276 | # output the json file 277 | json.dump(final_json, open(output_file, "w"), indent=4) 278 | 279 | 280 | if __name__ == "__main__": 281 | # reorder the columns as and when needed 282 | columns = [ 283 | "serial", 284 | "course_code", 285 | "course_name", 286 | "L", 287 | "P", 288 | "U", 289 | "section", 290 | "instructor", 291 | "room", 292 | "days", 293 | "hours", 294 | "midsem", 295 | "compre", 296 | ] 297 | 298 | timetable = pd.read_csv("./files/output.csv") 299 | 300 | create_json_file(timetable, columns, "./files/timetable.json", 2024, 2024, 1) 301 | -------------------------------------------------------------------------------- /src/files/timetable.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/crux-bphc/chronoscript/1141ea412b7c22dfaade51cfcfa6c77450dbeeb7/src/files/timetable.pdf -------------------------------------------------------------------------------- /src/parse_times.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | 4 | def parse_time(time: str, year: int, midsem=True) -> str: 5 | """ 6 | Function to parse the time from the string given by ttd, to ISO format for easier consumption in the web site. 7 | 8 | Args: 9 | time (str): The string given by ttd for the time. (Format: "13/03 11.30 - 1.00PM") 10 | year (int): The year of the timetable. (example: 2023) 11 | 12 | Returns: 13 | str: The time in ISO format. (example: "2023-03-13T11:30:00|2023-03-13T13:00:00") 14 | """ 15 | if midsem: 16 | time = time.split() 17 | 18 | # New 2024-25 Draft timetable for sem 2 doesn't have an extra '-' to be removed 19 | #time.pop(1) 20 | 21 | time = " ".join(time) 22 | 23 | parts = time.split() 24 | 25 | date_in_year = parts[0].split("/") 26 | 27 | date = date_in_year[0] + "-" + date_in_year[1] + "-" + str(year) 28 | 29 | start = parts[1].split(".") 30 | end = parts[3].split(".") 31 | 32 | end_time = end[1][-2:] 33 | end[1] = end[1][:-2] 34 | 35 | start_pm = start.copy() 36 | 37 | if end_time == "PM": 38 | # as hours are from 0 to 23, we add 12 to the hours to get the correct time 39 | if end[0] != "12": 40 | end[0] = str(int(end[0]) + 12) 41 | if start_pm[0] != "12": 42 | start_pm[0] = str(int(start_pm[0]) + 12) 43 | 44 | # mostly unnecessary, but for the sake of completeness 45 | if end_time == "AM" and end[0] == "12": 46 | end[0] = "00" 47 | 48 | # consider both cases if end time is PM 49 | # assumption is exam doesn't last longer than 12 hours (sane) 50 | # so if end time is PM and start_pm is less than end, then start_pm is correct start 51 | # otherwise start/start_am is correct start 52 | start_date_am = date + "T" + start[0] + ":" + start[1] + ":00" 53 | start_date_pm = date + "T" + start_pm[0] + ":" + start_pm[1] + ":00" 54 | end_date = date + "T" + end[0] + ":" + end[1] + ":00" 55 | 56 | end_date = pd.to_datetime(end_date, dayfirst=True).isoformat() 57 | start_date_am = pd.to_datetime(start_date_am, dayfirst=True).isoformat() 58 | start_date_pm = pd.to_datetime(start_date_pm, dayfirst=True).isoformat() 59 | 60 | # print(start_date_am, start_date_pm, end_date) 61 | 62 | if end_time == "PM": 63 | if pd.Timestamp(start_date_pm) > pd.Timestamp(end_date): 64 | start_date_am = pd.Timestamp(start_date_am) 65 | return ( 66 | ( 67 | pd.Timestamp(start_date_am) - pd.Timedelta(hours=5, minutes=30) 68 | ).isoformat() 69 | + "|" 70 | + ( 71 | pd.Timestamp(end_date) - pd.Timedelta(hours=5, minutes=30) 72 | ).isoformat() 73 | ) 74 | 75 | else: 76 | start_date_pm = pd.Timestamp(start_date_pm) 77 | return ( 78 | str( 79 | ( 80 | pd.Timestamp(start_date_pm) - pd.Timedelta(hours=5, minutes=30) 81 | ).isoformat() 82 | ) 83 | + "|" 84 | + ( 85 | pd.Timestamp(end_date) - pd.Timedelta(hours=5, minutes=30) 86 | ).isoformat() 87 | ) 88 | 89 | else: 90 | start_date_am = pd.Timestamp(start_date_am) 91 | return ( 92 | ( 93 | pd.Timestamp(start_date_am) - pd.Timedelta(hours=5, minutes=30) 94 | ).isoformat() 95 | + "|" 96 | + (pd.Timestamp(end_date) - pd.Timedelta(hours=5, minutes=30)).isoformat() 97 | ) 98 | 99 | 100 | def parse_compre_time(compre_time: str, year: int): 101 | """ 102 | Function to parse the compre time from the string given by ttd, to ISO format for easier consumption in the web site. 103 | 104 | Args: 105 | compre_time (str): The string given by ttd for the compre time. 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-------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "chronoscript" 3 | version = "0.1.0" 4 | description = "" 5 | authors = ["majimearun "] 6 | readme = "README.md" 7 | 8 | [tool.poetry.dependencies] 9 | python = "^3.10" 10 | pdfplumber = "^0.10.2" 11 | pandas = "^2.2.2" 12 | black = "^23.7.0" 13 | tabulate = "^0.9.0" 14 | 15 | 16 | [build-system] 17 | requires = ["poetry-core"] 18 | build-backend = "poetry.core.masonry.api" 19 | -------------------------------------------------------------------------------- /src/timetables.py: -------------------------------------------------------------------------------- 1 | import json 2 | from itertools import product, combinations 3 | from operator import itemgetter 4 | from typing import Annotated 5 | 6 | 7 | def get_filtered_json( 8 | json: Annotated[dict, "main timetable json file"], 9 | CDCs: Annotated[list[str], "list of BITS codes for CDCs selected"], 10 | DEls: Annotated[list[str], "list of BITS codes for DEls selected"], 11 | HUELs: Annotated[list[str], "list of BITS codes for HUELs selected"], 12 | OPELs: Annotated[list[str], "list of BITS codes for OPELs selected"], 13 | ) -> dict: 14 | """ 15 | Function to filter the main timetable json file to only include the selected courses 16 | 17 | Args: 18 | json (dict): main timetable json file 19 | CDCs (list[str]): list of BITS codes for CDCs selected 20 | DEls (list[str]): list of BITS codes for DEls selected 21 | HUELs (list[str]): list of BITS codes for HUELs selected 22 | OPELs (list[str]): list of BITS codes for OPELs selected 23 | 24 | Returns: 25 | dict: filtered json file, i.e, with only courses selected 26 | """ 27 | json = json["courses"] 28 | filtered_json = {"CDCs": {}, "DEls": {}, "HUELs": {}, "OPELs": {}} 29 | for CDC in CDCs: 30 | filtered_json["CDCs"][CDC] = json[CDC] 31 | for DEL in DEls: 32 | filtered_json["DEls"][DEL] = json[DEL] 33 | for HUEL in HUELs: 34 | filtered_json["HUELs"][HUEL] = json[HUEL] 35 | for OPEL in OPELs: 36 | filtered_json["OPELs"][OPEL] = json[OPEL] 37 | return filtered_json 38 | 39 | 40 | def separate_sections_into_types( 41 | filtered_json: Annotated[ 42 | dict, "filtered json file, i.e, with only courses selected" 43 | ] 44 | ) -> dict: 45 | """ 46 | Function to separate the sections into lectures, tutorials and practicals 47 | 48 | Args: 49 | filtered_json (dict): filtered json file, i.e, with only courses selected 50 | 51 | Returns: 52 | dict: dictionary of courses' sections separated into lectures, tutorials and practicals 53 | """ 54 | sep = {} 55 | 56 | for type in filtered_json: 57 | sep[type] = {} 58 | for course in filtered_json[type]: 59 | lectures = [] 60 | tutorials = [] 61 | practicals = [] 62 | # inner dictionary we'll be continuously referring to 63 | ref = filtered_json[type][course] 64 | for section in ref["sections"]: 65 | if section.startswith("L"): 66 | lectures.append(section) 67 | elif section.startswith("T"): 68 | tutorials.append(section) 69 | elif section.startswith("P"): 70 | practicals.append(section) 71 | sep[type][course] = { 72 | "L": lectures, 73 | "T": tutorials, 74 | "P": practicals, 75 | } 76 | # if list is empty remove the key-value pair 77 | # we need to remove it as it causes problems when using woth itertools.product() 78 | if not lectures: 79 | del sep[type][course]["L"] 80 | if not tutorials: 81 | del sep[type][course]["T"] 82 | if not practicals: 83 | del sep[type][course]["P"] 84 | return sep 85 | 86 | 87 | def generate_intra_combinations( 88 | filtered_json: Annotated[ 89 | dict, "filtered json file, i.e, with only courses selected" 90 | ], 91 | ) -> dict: 92 | """ 93 | Function that generates all possible combinations of sections within each course 94 | 95 | Args: 96 | filtered_json (dict): filtered json file, i.e, with only courses selected 97 | 98 | Returns: 99 | dict: dictionary of all possible combinations of sections within each course 100 | """ 101 | 102 | sep = separate_sections_into_types(filtered_json) 103 | combs = {} 104 | for type in sep: 105 | combs[type] = {} 106 | for course in sep[type]: 107 | sections = [] 108 | # first check is the type of section (L, T or P) is present in the course 109 | if sep[type][course].get("L") is not None: 110 | # list of lecture sections 111 | sections.append(sep[type][course]["L"]) 112 | if sep[type][course].get("P") is not None: 113 | # list of practical sections 114 | sections.append(sep[type][course]["P"]) 115 | if sep[type][course].get("T") is not None: 116 | # list of tutorial sections 117 | sections.append(sep[type][course]["T"]) 118 | # generate all possible combinations of sections (exhaustive and inclusive of clashes) 119 | combs[type][course] = list(product(*sections)) 120 | return combs 121 | 122 | 123 | def generate_exhaustive_timetables( 124 | filtered_json: Annotated[ 125 | dict, "filtered json file, i.e, with only courses selected" 126 | ], 127 | n_dels: Annotated[int, "number of DELs selected"], 128 | n_opels: Annotated[int, "number of OPELs selected"], 129 | n_huels: Annotated[int, "number of HUELs selected"], 130 | ) -> list: 131 | """ 132 | Function that generates all possible timetables (exhaustive and inclusive of clashes) 133 | 134 | Args: 135 | filtered_json (dict): filtered json file, i.e, with only courses selected 136 | 137 | Returns: 138 | list: list of all possible timetables (exhaustive and inclusive of clashes) 139 | """ 140 | 141 | combs = generate_intra_combinations(filtered_json) 142 | timetables = [] 143 | cdcs = [] 144 | dels = [] 145 | opels = [] 146 | huels = [] 147 | for type in combs: 148 | for course in combs[type]: 149 | # format (course, section combination for that course) 150 | if type == "CDCs": 151 | cdcs.append([(str(course), comb) for comb in combs[type][course]]) 152 | elif type == "DEls": 153 | dels.append([(str(course), comb) for comb in combs[type][course]]) 154 | elif type == "OPELs": 155 | opels.append([(str(course), comb) for comb in combs[type][course]]) 156 | elif type == "HUELs": 157 | huels.append([(str(course), comb) for comb in combs[type][course]]) 158 | else: 159 | raise Exception("Course type not found in any category") 160 | 161 | # choose n_dels from dels 162 | if dels: 163 | dels = list(combinations(dels, n_dels)) 164 | dels = [[j[0] for j in i] for i in dels] 165 | if huels: 166 | huels = list(combinations(huels, n_huels)) 167 | huels = [[j[0] for j in i] for i in huels] 168 | if opels: 169 | opels = list(combinations(opels, n_opels)) 170 | opels = [[j[0] for j in i] for i in opels] 171 | 172 | required = [dels, huels, opels] 173 | required = [i for i in required if i] 174 | possible_combinations_temp = list(product(*required)) 175 | possible_combinations = [] 176 | for i in possible_combinations_temp: 177 | combination = [] 178 | for j in i: 179 | combination.extend(j) 180 | possible_combinations.append(combination) 181 | courses = [] 182 | 183 | for comb in possible_combinations: 184 | poss = [] 185 | poss.extend(cdcs) 186 | poss.extend([[c] for c in comb]) 187 | courses.append(poss) 188 | 189 | timetables = list(product(*courses)) 190 | timetables = [] 191 | for i in range(len(courses)): 192 | timetables.extend(list(product(*courses[i]))) 193 | return timetables 194 | 195 | # timetables = list(product(cdcs, dels, huels, opels)) 196 | # return timetables 197 | 198 | 199 | def remove_clashes( 200 | timetables: Annotated[list, "exhaustive list of all possible timetables"], 201 | json: Annotated[dict, "filtered json file"], 202 | ) -> list: 203 | """ 204 | Function that filters out timetables with clashes 205 | 206 | Args: 207 | timetables (list): exhaustive list of all possible timetables 208 | json (dict): filtered json file 209 | 210 | Returns: 211 | list: list of timetables without clashes 212 | """ 213 | filtered = [] 214 | for timetable in timetables: 215 | # times currently held as "in use" by some course's section 216 | # format "DH" where D is the day and H is the hour 217 | times: dict[str, bool] = dict() 218 | clashes = False 219 | for course in timetable: 220 | # course[1] as that has the section details, course[0] hold course code 221 | for sec in course[1]: 222 | # the schedule of the section from the main json file 223 | if course[0] in json["CDCs"]: 224 | sched = json["CDCs"][course[0]]["sections"][sec]["schedule"] 225 | elif course[0] in json["DEls"]: 226 | sched = json["DEls"][course[0]]["sections"][sec]["schedule"] 227 | elif course[0] in json["HUELs"]: 228 | sched = json["HUELs"][course[0]]["sections"][sec]["schedule"] 229 | elif course[0] in json["OPELs"]: 230 | sched = json["OPELs"][course[0]]["sections"][sec]["schedule"] 231 | else: 232 | raise Exception("Course code not found in any category") 233 | # ts denotes all slots needed for the section 234 | ts = [] 235 | for i in range(len(sched)): 236 | ts.extend(list(product(sched[i]["days"], sched[i]["hours"]))) 237 | # converting it to the string of required format "DH" 238 | ts = [str(t[0]) + str(t[1]) for t in ts] 239 | # if any slot in ts is already in times, then there is a clash 240 | # if so, mark it as clashes and dont add it to the filtered list 241 | for t in ts: 242 | if times.get(t) is not None: 243 | clashes = True 244 | break 245 | else: 246 | times[t] = True 247 | if clashes: 248 | break 249 | if clashes: 250 | break 251 | # if no clashes, add it to the filtered list 252 | if not clashes: 253 | filtered.append(timetable) 254 | 255 | return filtered 256 | 257 | 258 | def remove_exam_clashes( 259 | timetables: Annotated[list, "list of timetables without any clashes (classes)"], 260 | json: Annotated[dict, "filtered json file"], 261 | ): 262 | """ 263 | Function that filters out timetables with exam clashes. 264 | 265 | Args: 266 | timetables (list): list of timetables without any clashes (classes) 267 | json (dict): filtered json file 268 | 269 | Returns: 270 | list: list of timetables without any clashes (classes and exams) 271 | """ 272 | no_exam_clashes = [] 273 | for timetable in timetables: 274 | mids_times: dict[str, int] = dict() 275 | compres_times: dict[str, int] = dict() 276 | clashes = False 277 | for course in timetable: 278 | # get exam times 279 | if course[0] in json["CDCs"]: 280 | mid = json["CDCs"][course[0]]["exams"][0].get("midsem", "") 281 | compre = json["CDCs"][course[0]]["exams"][0].get("compre", "") 282 | elif course[0] in json["DEls"]: 283 | mid = json["DEls"][course[0]]["exams"][0].get("midsem", "") 284 | compre = json["DEls"][course[0]]["exams"][0].get("compre", "") 285 | elif course[0] in json["HUELs"]: 286 | mid = json["HUELs"][course[0]]["exams"][0].get("midsem", "") 287 | compre = json["HUELs"][course[0]]["exams"][0].get("compre", "") 288 | elif course[0] in json["OPELs"]: 289 | mid = json["OPELs"][course[0]]["exams"][0].get("midsem", "") 290 | compre = json["OPELs"][course[0]]["exams"][0].get("compre", "") 291 | else: 292 | raise Exception("Course code not found in any category") 293 | mids_times[mid] = mids_times.get(mid, 0) + 1 294 | compres_times[compre] = compres_times.get(compre, 0) + 1 295 | # see if more than one course has the same exam time 296 | for time in mids_times: 297 | if mids_times[time] > 1 and time != None: 298 | clashes = True 299 | break 300 | if not clashes: 301 | for time in compres_times: 302 | if compres_times[time] > 1 and time != None: 303 | clashes = True 304 | break 305 | # for i in range(len(mids_times)): 306 | # for j in range(i + 1, len(mids_times)): 307 | # if mids_times[i] == mids_times[j]: 308 | # clashes = True 309 | # break 310 | # if clashes: 311 | # break 312 | # if not clashes: 313 | # for i in range(len(compres_times)): 314 | # for j in range(i + 1, len(compres_times)): 315 | # if compres_times[i] == compres_times[j]: 316 | # clashes = True 317 | # break 318 | # if clashes: 319 | # break 320 | # add to filtered list only if no clashes 321 | if not clashes: 322 | no_exam_clashes.append(timetable) 323 | return no_exam_clashes 324 | 325 | 326 | def day_wise_filter( 327 | timetables: Annotated[list, "list of timetables without clashes"], 328 | json: Annotated[dict, "filtered json file"], 329 | free_days: Annotated[list[str], "list of days to be free if possible"], 330 | lite_order: Annotated[ 331 | list[str], 332 | "increasing order of how lite you want days to be (earlier means more lite)", 333 | ], 334 | filter: Annotated[bool, "whether to filter or to just sort"] = False, 335 | strong: Annotated[bool, "whether to use strong filter or not"] = False, 336 | ) -> list: 337 | """ 338 | Function that filters out timetables based on the number of free days and the lite order. Lite order is the order in which you want the days to be lite. For example, if you want Saturday to be the most lite day, then lite_order = ["S", "Su", "M", "T", "W", "Th", "F"] (set the order of the other 6 accordingly)) 339 | 340 | Args: 341 | timetables (list): list of timetables without clashes 342 | json (dict): filtered json file, i.e, with only courses selected 343 | free_days (list): list of days to be free if possible 344 | lite_order (list): increasing order of how lite you want days to be (earlier means more lite) 345 | filter (bool, optional): whether to filter or to just sort. Defaults to False. 346 | strong (bool, optional): whether to use strong filter or not. Defaults to False. 347 | 348 | Returns: 349 | list: list of timetables after filtering. They are sorted based on how many of the free days they have and how lite the days are. 350 | """ 351 | # format: (n days matched free, timetable) 352 | matches_free_days: list[tuple] = [] 353 | # format: (daily scores in a list [0, 4, 5, ...], timetable) 354 | others: list[tuple] = [] 355 | 356 | day_dict = { 357 | "M": 0, 358 | "T": 1, 359 | "W": 2, 360 | "Th": 3, 361 | "F": 4, 362 | "S": 5, 363 | "Su": 6, 364 | } 365 | 366 | for timetable in timetables: 367 | # will contain the hours of each day where there is a class 368 | # used for calculating the daily scores and if it matches the free days 369 | schedule = { 370 | "M": [], 371 | "T": [], 372 | "W": [], 373 | "Th": [], 374 | "F": [], 375 | "S": [], 376 | "Su": [], 377 | } 378 | for course in timetable: 379 | for sec in course[1]: 380 | # getting the schedule of the selected section 381 | if course[0] in json["CDCs"]: 382 | sched = json["CDCs"][course[0]]["sections"][sec]["schedule"] 383 | elif course[0] in json["DEls"]: 384 | sched = json["DEls"][course[0]]["sections"][sec]["schedule"] 385 | elif course[0] in json["HUELs"]: 386 | sched = json["HUELs"][course[0]]["sections"][sec]["schedule"] 387 | elif course[0] in json["OPELs"]: 388 | sched = json["OPELs"][course[0]]["sections"][sec]["schedule"] 389 | else: 390 | raise Exception("Course code not found in any category") 391 | # since no clashes, we can just append the hours to the schedule 392 | for i in range(len(sched)): 393 | for day in sched[i]["days"]: 394 | schedule[day].append(sched[i]["hours"]) 395 | # calculating the daily scores 396 | daily_scores = [len(v) for k, v in schedule.items()] 397 | # reordering the daily scores to match the lite order 398 | daily_scores = [daily_scores[day_dict[day]] for day in lite_order] 399 | 400 | n_free = 0 401 | for day in free_days: 402 | if len(schedule[day]) == 0: 403 | n_free += 1 404 | 405 | # if not strong filter, then if atleast some of the required free days are free, then add it to the list 406 | if n_free > 0 and not strong: 407 | matches_free_days.append((n_free, daily_scores, timetable)) 408 | elif n_free == len(free_days): 409 | matches_free_days.append((n_free, daily_scores, timetable)) 410 | else: 411 | others.append((n_free, daily_scores, timetable)) 412 | 413 | # sorting based on the number of free days (descending) and then the daily scores (ascending) 414 | matches_free_days = sorted(matches_free_days, key=itemgetter(0), reverse=True) 415 | matches_free_days = sorted(matches_free_days, key=itemgetter(1)) 416 | 417 | others = sorted(others, key=itemgetter(0), reverse=True) 418 | others = sorted(others, key=itemgetter(1)) 419 | 420 | # reorder back to original order (M, T, W, Th, F, S, Su) 421 | original_order = { 422 | "M": lite_order.index("M"), 423 | "T": lite_order.index("T"), 424 | "W": lite_order.index("W"), 425 | "Th": lite_order.index("Th"), 426 | "F": lite_order.index("F"), 427 | "S": lite_order.index("S"), 428 | "Su": lite_order.index("Su"), 429 | } 430 | for i in range(len(matches_free_days)): 431 | matches_free_days[i] = ( 432 | matches_free_days[i][0], 433 | [matches_free_days[i][1][original_order[day]] for day in original_order], 434 | matches_free_days[i][2], 435 | ) 436 | 437 | for i in range(len(others)): 438 | others[i] = ( 439 | others[i][0], 440 | [others[i][1][original_order[day]] for day in original_order], 441 | others[i][2], 442 | ) 443 | 444 | if filter: 445 | return [i for i in matches_free_days] 446 | 447 | else: 448 | return [i for i in matches_free_days] + [i for i in others] 449 | 450 | 451 | def export_to_json(timetables: list, filtered_json: dict, n_export: int = 100) -> None: 452 | """ 453 | Function that exports your timetables to a json file (in the sorted order) 454 | 455 | Args: 456 | timetables (list): list of timetables 457 | filtered_json (dict): filtered json file, i.e, with only courses selected 458 | n_export (int, optional): number of timetables to export. Defaults to 100. 459 | 460 | Returns: 461 | None 462 | """ 463 | export = [] 464 | for timetable in timetables: 465 | export_tt = {} 466 | export_tt["free_matched"] = timetable[0] 467 | export_tt["daily_scores"] = timetable[1] 468 | export_tt["timetable"] = {} 469 | for course in timetable[2]: 470 | export_tt["timetable"][course[0]] = {} 471 | export_tt["timetable"][course[0]]["sections"] = {} 472 | for sec in course[1]: 473 | export_tt["timetable"][course[0]]["sections"][sec] = {} 474 | export_tt["timetable"][course[0]]["sections"][sec]["schedule"] = [] 475 | if course[0] in filtered_json["CDCs"]: 476 | sched = filtered_json["CDCs"][course[0]]["sections"][sec][ 477 | "schedule" 478 | ] 479 | elif course[0] in filtered_json["DEls"]: 480 | sched = filtered_json["DEls"][course[0]]["sections"][sec][ 481 | "schedule" 482 | ] 483 | elif course[0] in filtered_json["HUELs"]: 484 | sched = filtered_json["HUELs"][course[0]]["sections"][sec][ 485 | "schedule" 486 | ] 487 | elif course[0] in filtered_json["OPELs"]: 488 | sched = filtered_json["OPELs"][course[0]]["sections"][sec][ 489 | "schedule" 490 | ] 491 | else: 492 | raise Exception("Course code not found in any category") 493 | for i in range(len(sched)): 494 | export_tt["timetable"][course[0]]["sections"][sec][ 495 | "schedule" 496 | ].append( 497 | { 498 | "days": sched[i]["days"], 499 | "hours": sched[i]["hours"], 500 | } 501 | ) 502 | if course[0] in filtered_json["CDCs"]: 503 | exam = filtered_json["CDCs"][course[0]]["exams"][0] 504 | elif course[0] in filtered_json["DEls"]: 505 | exam = filtered_json["DEls"][course[0]]["exams"][0] 506 | elif course[0] in filtered_json["HUELs"]: 507 | exam = filtered_json["HUELs"][course[0]]["exams"][0] 508 | elif course[0] in filtered_json["OPELs"]: 509 | exam = filtered_json["OPELs"][course[0]]["exams"][0] 510 | else: 511 | raise Exception("Course code not found in any category") 512 | export_tt["timetable"][course[0]]["exams"] = exam 513 | export.append(export_tt) 514 | if len(export) == n_export: 515 | break 516 | json.dump(export, open("./files/my_timetables.json", "w"), indent=4) 517 | 518 | 519 | if __name__ == "__main__": 520 | # need to get these as inputs 521 | CDCs = [ 522 | "CS F213", 523 | "CS F214", 524 | "CS F215", 525 | "CS F222", 526 | "ECON F311", 527 | "ECON F312", 528 | "ECON F313", 529 | ] 530 | 531 | # Order the oreference of DELs, HUELs and OPELs 532 | 533 | DEls = [] 534 | 535 | nDels = len(DEls) 536 | 537 | OPELs = [] 538 | 539 | nOpels = len(OPELs) 540 | 541 | HUELs = [] 542 | 543 | nHuels = len(HUELs) 544 | 545 | pref = ["DEls", "OPELs", "HUELs"] 546 | 547 | free_days = ["S"] 548 | 549 | lite_order = ["S", "Su", "M", "T", "W", "Th", "F"] 550 | 551 | # load the json file created 552 | tt_json = json.load(open("./files/timetable.json", "r")) 553 | 554 | filtered_json = get_filtered_json(tt_json, CDCs, DEls, HUELs, OPELs) 555 | 556 | exhaustive_list_of_timetables = generate_exhaustive_timetables( 557 | filtered_json, nDels, nOpels, nHuels 558 | ) 559 | 560 | timetables_without_clashes = remove_clashes( 561 | exhaustive_list_of_timetables, filtered_json 562 | ) 563 | 564 | print( 565 | "Number of timetables without clashes (classes):", 566 | len(timetables_without_clashes), 567 | ) 568 | 569 | timetables_without_clashes = remove_exam_clashes( 570 | timetables_without_clashes, filtered_json 571 | ) 572 | 573 | print( 574 | "Number of timetables without clashes (classes and exams):", 575 | len(timetables_without_clashes), 576 | ) 577 | 578 | in_my_preference_order = day_wise_filter( 579 | timetables_without_clashes, 580 | filtered_json, 581 | free_days, 582 | lite_order, 583 | filter=False, 584 | strong=False, 585 | ) 586 | 587 | print("Number of timetables after filter: ", len(in_my_preference_order)) 588 | 589 | if len(in_my_preference_order) > 0: 590 | print( 591 | "-----------------------------------------------------", 592 | "\nHighest match:\n", 593 | in_my_preference_order[0], 594 | "\n\n", 595 | "-----------------------------------------------------", 596 | "\nLowest match:\n", 597 | in_my_preference_order[-1], 598 | ) 599 | else: 600 | print("No timetables found") 601 | 602 | export_to_json(in_my_preference_order, filtered_json) 603 | -------------------------------------------------------------------------------- /src/visualize.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import tabulate 4 | 5 | conversion_dict = { 6 | 1: "8 - 8:50AM", 7 | 2: "9 - 9:50AM", 8 | 3: "10 - 10:50AM", 9 | 4: "11 - 11:50AM", 10 | 5: "12 - 12:50PM", 11 | 6: "1 - 1:50PM", 12 | 7: "2 - 2:50PM", 13 | 8: "3 - 3:50PM", 14 | 9: "4 - 4:50PM", 15 | 10: "5 - 5:50PM", 16 | 11: "6 - 6:50PM", 17 | 12: "7 - 7:50PM", 18 | 13: "8 - 8:50PM", 19 | 14: "9 - 9:50PM", 20 | } 21 | 22 | 23 | def convert_timetable_to_pandas_dataframe( 24 | timetables: list[dict], index: int, condensed: bool = True 25 | ): 26 | """ 27 | Function to convert timetable to pandas dataframe for better visualization 28 | 29 | Args: 30 | timetables (list[dict]): List of timetables 31 | index (int): Index of timetable to be converted to pandas dataframe 32 | condensed (bool, optional): Whether to condense the dataframe or not. Defaults to True. 33 | 34 | Returns: 35 | class_df (pd.DataFrame): Dataframe containing class schedule 36 | midsem_df (pd.DataFrame): Dataframe containing midsem schedule 37 | compre_df (pd.DataFrame): Dataframe containing compre schedule 38 | """ 39 | timetable = timetables[index] 40 | timetable = timetable["timetable"] 41 | if condensed: 42 | class_df = pd.DataFrame(columns=["Course", "Section", "Days", "Time"]) 43 | else: 44 | class_df = pd.DataFrame( 45 | columns=[ 46 | "8 - 8:50AM", 47 | "9 - 9:50AM", 48 | "10 - 10:50AM", 49 | "11 - 11:50AM", 50 | "12 - 12:50PM", 51 | "1 - 1:50PM", 52 | "2 - 2:50PM", 53 | "3 - 3:50PM", 54 | "4 - 4:50PM", 55 | ], 56 | index=["M", "T", "W", "Th", "F", "S"], 57 | ) 58 | midsem_df = pd.DataFrame(columns=["Course", "Date", "Time"]) 59 | compre_df = pd.DataFrame(columns=["Course", "Date", "Time"]) 60 | 61 | for course in timetable: 62 | for section in timetable[course]["sections"]: 63 | for schedule in timetable[course]["sections"][section]["schedule"]: 64 | if condensed: 65 | temp = pd.DataFrame( 66 | { 67 | "Course": course, 68 | "Section": section, 69 | "Days": " ".join(schedule["days"]), 70 | "Time": ", ".join( 71 | [conversion_dict[i] for i in schedule["hours"]] 72 | ), 73 | }, 74 | index=[0], 75 | ) 76 | class_df = pd.concat([class_df, temp]) 77 | else: 78 | for day in schedule["days"]: 79 | for hour in schedule["hours"]: 80 | class_df.loc[day, conversion_dict[hour]] = ( 81 | course + " " + section 82 | ) 83 | exam_details = timetable[course]["exams"] 84 | 85 | temp = pd.DataFrame( 86 | { 87 | "Course": course, 88 | "Date": exam_details["midsem"].split(" ")[0], 89 | "Time": " ".join(exam_details["midsem"].split(" ")[1:]), 90 | }, 91 | index=[0], 92 | ) 93 | 94 | midsem_df = pd.concat([midsem_df, temp]) 95 | 96 | temp = pd.DataFrame( 97 | { 98 | "Course": course, 99 | "Date": exam_details["compre"].split(" ")[0], 100 | "Time": " ".join(exam_details["compre"].split(" ")[1:]), 101 | }, 102 | index=[0], 103 | ) 104 | 105 | compre_df = pd.concat([compre_df, temp]) 106 | if condensed: 107 | class_df = class_df.sort_values(by=["Days", "Time"]) 108 | class_df.reset_index(drop=True, inplace=True) 109 | class_df.index += 1 110 | 111 | else: 112 | class_df.fillna("", inplace=True) 113 | 114 | midsem_df = midsem_df.sort_values(by=["Date"]) 115 | midsem_df.reset_index(drop=True, inplace=True) 116 | midsem_df.index += 1 117 | 118 | compre_df = compre_df.sort_values(by=["Date"]) 119 | compre_df.reset_index(drop=True, inplace=True) 120 | compre_df.index += 1 121 | 122 | return class_df, midsem_df, compre_df 123 | 124 | 125 | if __name__ == "__main__": 126 | index = 0 127 | with open("./files/my_timetables.json", "r") as f: 128 | timetables = json.load(f) 129 | dfs = convert_timetable_to_pandas_dataframe(timetables, index, False) 130 | print("======================================================\n") 131 | print("Class Schedule:\n\n") 132 | print(tabulate.tabulate(dfs[0], headers="keys", tablefmt="fancy_grid")) 133 | print("------------------------------------------------------\n") 134 | print("\nMidsem Schedule:\n\n") 135 | print(tabulate.tabulate(dfs[1], headers="keys", tablefmt="fancy_grid")) 136 | print("------------------------------------------------------\n") 137 | print("\nCompre Schedule:\n\n") 138 | print(tabulate.tabulate(dfs[2], headers="keys", tablefmt="fancy_grid")) 139 | print("======================================================\n") 140 | --------------------------------------------------------------------------------