Merge pull request #13 from BeneLuWi/development

Add Some Meta Infos and License

CHANGELOG.md

@@ -0,0 +1,12 @@
+## 0.1.1
+
+_03.01.23_
+
+- Allow the deletion of `Tasks`
+- Allow decimals for `Task` points
+
+## 0.1.0
+
+_02.01.23_
+
+- Initial Release

LICENSE

@@ -0,0 +1,9 @@
+The MIT License (MIT)
+
+Copyright © 2023 Benedikt Lüken-Winkels and David Kaub
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,17 +1,57 @@
-# Examanager Version 3
+<p align="center">
+  <a href="https://github.com/BeneLuWi/examanager_v3"><img src="docs/Examanager-logos-wide.jpeg" alt="Examanager"></a>
+</p>
 
-Examanager is a web app for teachers to manage exams and their classes’ results. It allows viewing and editing exam results, statistical analysis and data export to `Excel`
+<p align="center">
+  <a href="https://hub.docker.com/r/beneluwi/examanager" target="_blank">
+      <img src="https://img.shields.io/docker/v/beneluwi/examanager?color=%23086dd7&logo=Docker" alt="Docker">
+  </a>
+</p>
+
+<hr/>
+
+Examanager is a web app for teachers to manage exams and their classes’ results. It allows viewing and editing exam
+results, statistical analysis and data export to `Excel`
 
 ## Quick Start
 
 ```shell
 git clone https://github.com/BeneLuWi/examanager_v3.git
 cd examanager_v3
-docker compose up
 ```
 
-visit `http://localhost:5200`
+- Initialize the `admin` user by adding the environment variable `INIT_ADMIN_USER: true` to the `examanager` service
+
+```shell
+docker compose up -d
+```
+
+- Visit `http://localhost:5200` and login with `admin:ChangeMe`
+- Visit the API Docs [Swagger UI](http://localhost:5200/docs)
+
+## Development Setup
+
+You need: `docker compose`, `python@3.10`, `poerty`, `node`, `npm`
+
+- Start Database
+
+```shell
+docker compose -f docker-compose-dev.yml up mongodb
+```
+
+- Start server
 
-## API-Docs
+```shell
+cd server
+poetry install
+poetry shell
+uvicorn server.main:app --port 5200
+```
+
+- Start app
 
-[Swagger UI](http://localhost:5200/docs)
+```shell
+cd app
+npm install
+npm start
+```

VERSION

@@ -0,0 +1 @@
+0.1.1

docker-compose-prod.yml → docker-compose-dev.yml

@@ -2,8 +2,10 @@ version: "3.9"
 
 services:
   mongodb:
-    image: mongo:4.4.6
+    image: mongo
     container_name: examanager-db
+    ports:
+      - "27017:27017"
 
   examanager:
     build:
@@ -12,7 +14,6 @@ services:
     container_name: examanager-app
     environment:
       MONGO_HOST: "mongodb"
-      INIT_ADMIN_USER: true
     ports:
       - "5200:5200"
     depends_on:

docker-compose.yml

@@ -4,13 +4,9 @@ services:
   mongodb:
     image: mongo
     container_name: examanager-db
-    ports:
-      - "27017:27017"
 
   examanager:
-    build:
-      context: .
-      dockerfile: Dockerfile
+    image: beneluwi/examanager
     container_name: examanager-app
     environment:
       MONGO_HOST: "mongodb"

server/server/api/api_v1/routers/statistics_api/utils.py

@@ -6,6 +6,7 @@
 from typing import List
 import pandas as pd
 from pydantic import ValidationError
+from starlette.background import BackgroundTask
 from starlette.responses import FileResponse
 
 from server.api.api_v1.routers.statistics_api.models import StatisticsResult, TaskResult, StatisticsElement
@@ -65,11 +66,6 @@ def create_student_results_dataframe(exam_results_response: ExamResultsResponse)
     student_results_df["Gesamtpunkte"] = student_results_df[task_names].sum(axis=1)
     student_results_df["Erreichte Punkte relativ"] = student_results_df["Gesamtpunkte"] / max_points_for_exam
 
-    # student_results_df = pd.concat([student_results_df, student_results_df.
-    #                               apply(lambda row: pd.Series(get_exam_rating_for_reached_percentage(
-    #    exam=exam_results_response.exam,
-    #    reached_percentage=row["Erreichte Punkte relativ"]).dict()), axis=1)], axis=0)
-
     rating_results = [
         get_exam_rating_for_reached_percentage(
             exam=exam_results_response.exam, reached_percentage=reached_percentage
@@ -160,7 +156,6 @@ def create_student_statistics_dataframe(exam_results_response: ExamResultsRespon
 
     # 10. Schwierigkeit
     reachable_per_task = pd.DataFrame({task.name: task.max_points for task in tasks}, columns=task_names, index=[0])
-    # reachable_per_task["Sorting"]="per_task"
     number_of_w = len(student_results_df[student_results_df["Geschlecht"] == "w"])
     number_of_d = len(student_results_df[student_results_df["Geschlecht"] == "d"])
     number_of_m = len(student_results_df[student_results_df["Geschlecht"] == "m"])
@@ -177,26 +172,23 @@ def create_student_statistics_dataframe(exam_results_response: ExamResultsRespon
     reachable_d["Geschlecht"] = "d"
 
     reachable_all = pd.concat([reachable_total, reachable_d, reachable_m, reachable_w])
-    reachable_all["Gesamtpunkte"] = reachable_all.sum(axis=1)
+    reachable_all["Gesamtpunkte"] = reachable_all.sum(axis=1, numeric_only=True)
 
     columns_for_difficulty = task_names.copy()
     columns_for_difficulty.append("Gesamtpunkte")
 
     reached_total = student_results_df[columns_for_difficulty].sum()
     reached_total = reached_total.to_frame().T
-    # reached_total["Statistik"] = "Summe erreichte Punkte"
     reached_total["Geschlecht"] = ""
 
     reached_by_gender = students_grouped_by_gender[columns_for_difficulty].sum().reset_index()
-    # reached_by_gender["Statistik"] = "Summe erreichte Punkte"
 
     reached_all = pd.concat([reached_total, reached_by_gender])
 
     difficulty_df = reached_all.set_index("Geschlecht").div(reachable_all.set_index("Geschlecht"), fill_value=0)
     difficulty_df = difficulty_df * 100
     difficulty_df.reset_index(inplace=True)
     difficulty_df = difficulty_df.assign(Statistik="Schwierigkeit")
-    # difficulty_df = reachable_all.subtract(reached_all, axis="columns")#  * 100
 
     # 11. Trennschärfe
 
@@ -317,12 +309,6 @@ def create_task_result_object(student_statistics_df: pd.DataFrame, columns_to_pr
 
 
 def create_statistics_result_object(student_statistics_df: pd.DataFrame, tasks: List[Task]) -> StatisticsResult:
-    # metric_names = ["Mittelwert (Mean)", "Mittelwert (Median)", "Schwierigkeit", "Trennschärfe"]
-    # for metric in metric_names:
-    #    create_task_result_object(student_statistics_df=student_statistics_df, columns_to_process=columns_to_process,
-    #                              metric_name=metric)#
-
-    # mittelwert mean und median für mss (und note) als getrennte Statistik
 
     task_names = [task.name for task in tasks]
 
@@ -371,7 +357,6 @@ def create_statistics_result_object(student_statistics_df: pd.DataFrame, tasks:
         metric_name="Trennschärfe",
     )
 
-    # todo self assessment will look differently
     self_assessment_result_mean: TaskResult = create_task_result_object(
         student_statistics_df=student_statistics_df,
         columns_to_process=["Selbsteinschätzung MSS", "Abweichung Selbsteinschätzung MSS"],
@@ -429,6 +414,17 @@ async def calculate_statistics_object(exam_results_response: ExamResultsResponse
     return statistics
 
 
+def _cleanup(folder: os.PathLike[str], file: os.PathLike[str]) -> None:
+    """
+    Cleanup after successfully returning the file
+    :param folder: Folder containing the File
+    :param file: Actual File
+    :return:
+    """
+    os.remove(file)
+    os.rmdir(folder)
+
+
 async def calculate_statistics_excel(exam_results_response: ExamResultsResponse, include_deactivated_tasks=False):
     """
     1. Get ratings for result
@@ -446,19 +442,15 @@ async def calculate_statistics_excel(exam_results_response: ExamResultsResponse,
         exam_results_response=exam_results_response, student_results_df=student_results_df
     )
 
-    print("student_statistics_df")
-    print(student_statistics_df.to_string())
-
-    # TODO Use tmp dir
-    folder_id = str(uuid.uuid1())
-    os.mkdir(os.path.join("data", folder_id))
-    path = os.path.join("data", folder_id, "Klausurergebnis.xlsx")
+    folder_path = os.path.join("data", str(uuid.uuid1()))
+    os.mkdir(folder_path)
+    file_path = os.path.join(folder_path, "Klausurergebnis.xlsx")
 
     # create an Excel writer object
-    with pd.ExcelWriter(path) as writer:
+    with pd.ExcelWriter(file_path) as writer:
         # use to_excel function and specify the sheet_name and index
         # to store the dataframe in specified sheet
         student_results_df.to_excel(writer, sheet_name="Ergebnisse", index=False)
         student_statistics_df.to_excel(writer, sheet_name="Statistik", index=False)
 
-    return FileResponse(path)
+    return FileResponse(file_path, background=BackgroundTask(_cleanup, folder_path, file_path))