initial commit: visualizer

Author: farman20ali

README.md

@@ -1,2 +1,100 @@
 # sorting-visualizer
  This Flask web app demonstrates the execution of sorting algorithms (Merge, Insertion, Selection etc.) Users can test algorithms on arrays provided directly, via files, or generated randomly, and compare performance on varying sizes. It features execution time visualizations (bar/line plots), result downloads, and aids in algorithm analysis.
+
+
+# Sorting Algorithm Performance Visualizer
+
+This Flask web application allows users to test and compare the performance of various sorting algorithms on arrays of different sizes and types. It includes features for result visualization and downloading.
+
+## Features
+
+- **Input Options**:
+  - Directly enter an array.
+  - Upload a file containing an array.
+  - Generate a random array of a specified size.
+  
+- **Execution Modes**:
+  - Test a single array with selected sorting algorithms.
+  - Compare multiple algorithms on arrays of varying sizes.
+  
+- **Visualization**:
+  - Bar chart to compare execution times for a single array.
+  - Line graph to show algorithm performance trends for varying input sizes.
+
+- **Download Results**: Retrieve execution results as a `.txt` file.
+
+## Algorithms Supported
+
+- Merge Sort
+- Insertion Sort
+- Selection Sort
+- Bubble Sort
+- Quick Sort
+
+## Installation
+
+### Prerequisites
+- Python 3.7 or higher
+- pip (Python package manager)
+
+### Setup
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/yourusername/sorting-visualizer.git
+   cd sorting-visualizer
+
+
+
+## Running the Application
+
+### 1. Create a Virtual Environment  
+Set up a Python virtual environment to isolate dependencies:
+```bash
+    python -m venv venv
+
+```
+Activate the environment:
+- On **Linux/Mac**:
+  ```bash
+  source venv/bin/activate
+  ```
+- On **Windows (CMD)**:
+  ```cmd
+  venv\Scripts\activate
+  ```
+- On **Windows (PowerShell)**:
+  ```powershell
+  .\venv\Scripts\Activate
+  ```
+
+### 2. Install Dependencies  
+Install the required Python packages using `requirements.txt`:
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Run the Application  
+Start the Flask application:
+```bash
+python app.py
+```
+
+
+### 4. Access the Application  
+Open your web browser and go to:
+```
+http://127.0.0.1:5000
+```
+
+---
+
+## Tags
+`Flask` `Sorting Algorithms` `Data Visualization` `Algorithm Comparison` `Python` `Bar Graph` `Line Graph` `Random Arrays`
+
+---
+
+## License
+This project is licensed under the [MIT License](LICENSE).
+```
+
+You can copy and paste this directly into your README file. Let me know if you need further adjustments!

__init__.py

@@ -0,0 +1,69 @@
+def merge_sort(arr):
+    if len(arr) > 1:
+        mid = len(arr) // 2
+        left_half = arr[:mid]
+        right_half = arr[mid:]
+
+        merge_sort(left_half)
+        merge_sort(right_half)
+
+        i = j = k = 0
+
+        while i < len(left_half) and j < len(right_half):
+            if int(left_half[i]) < int(right_half[j]):
+                arr[k] = left_half[i]
+                i += 1
+            else:
+                arr[k] = right_half[j]
+                j += 1
+            k += 1
+
+        while i < len(left_half):
+            arr[k] = left_half[i]
+            i += 1
+            k += 1
+
+        while j < len(right_half):
+            arr[k] = right_half[j]
+            j += 1
+            k += 1
+
+    return arr
+
+def insertion_sort(arr):
+    for i in range(1, len(arr)):
+        key = arr[i]
+        j = i - 1
+        while j >= 0 and int(key) < int(arr[j]):
+            arr[j + 1] = arr[j]
+            j -= 1
+        arr[j + 1] = key
+    return arr
+
+def selection_sort(arr):
+    for i in range(len(arr)):
+        min_idx = i
+        for j in range(i+1, len(arr)):
+            if int(arr[min_idx]) > int(arr[j]):
+                min_idx = j
+        arr[i], arr[min_idx] = arr[min_idx], arr[i]
+    return arr
+
+def bubble_sort(arr):
+    n = len(arr)
+    for i in range(n):
+        for j in range(0, n-i-1):
+            if arr[j] > arr[j+1]:
+                arr[j], arr[j+1] = arr[j+1], arr[j]
+    return arr
+
+
+def quick_sort(arr):
+    if len(arr) <= 1:
+        return arr
+    else:
+        pivot = arr[len(arr) // 2]
+        left = [x for x in arr if x < pivot]
+        middle = [x for x in arr if x == pivot]
+        right = [x for x in arr if x > pivot]
+        return quick_sort(left) + middle + quick_sort(right)

algorithms.py

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+from flask import Flask, render_template,request,flash,redirect,url_for,send_file
+from algorithms import *
+import time
+import matplotlib
+import io
+import base64 
+from random_array import generate_random_array
+import matplotlib.pyplot
+from io import BytesIO
+
+matplotlib.use('agg')  # Use the 'agg' backend (non-interactive) for Matplotlib
+
+#selection sort, insertion sort, and merge sort
+
+app = Flask(__name__)
+app.secret_key = 'sort_and_sort'  # Required for flash messages
+
+
+# Function to measure execution time
+def measure_execution_time(algorithm, arr, iterations=10):
+    total_time = 0
+    for _ in range(iterations):
+        arr_copy = arr.copy()
+        start_time = time.perf_counter()
+        sorted_arr=algorithm(arr_copy)
+        end_time = time.perf_counter()
+        total_time += end_time - start_time
+    return (total_time / iterations),sorted_arr
+ 
+@app.route('/download-text', methods=['POST'])
+def download_retrieved_text():
+    retrieved_text = request.form['results']
+    return send_file(BytesIO(retrieved_text.encode()), as_attachment=True, download_name='results.txt', mimetype='text/plain')
+
+
+# Plot function
+def plot_line_graph(input_sizes, execution_times, title):
+# Plot function
+    fig, ax = matplotlib.pyplot.subplots(figsize=(10, 8))
+    for alg_name, times in execution_times.items():
+        ax.plot(input_sizes, times, label=alg_name, marker='o')
+    ax.set_xlabel('Input Size')
+    ax.set_ylabel('Execution Time (seconds)')
+    ax.set_title(title)
+    ax.legend()
+    ax.grid(True)
+
+    buf = io.BytesIO()
+    matplotlib.pyplot.savefig(buf, format='png')
+    buf.seek(0)
+    plot_url = base64.b64encode(buf.getvalue()).decode('utf8')
+    matplotlib.pyplot.close(fig)
+    return plot_url
+
+def plot_bar_graph(execution_times):
+     # Plot the execution times
+    fig, ax = matplotlib.pyplot.subplots(figsize=(10, 8))
+    ax.bar(execution_times.keys(), execution_times.values(), width=0.4)
+    ax.set_title("Execution Time Comparison")
+    ax.set_xlabel("Algorithm")
+    ax.set_ylabel("Execution Time (seconds)")
+    matplotlib.pyplot.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability
+
+    # Save the plot to a bytes buffer
+    buf = io.BytesIO()
+    matplotlib.pyplot.savefig(buf, format='png')
+    buf.seek(0)
+    plot_url = base64.b64encode(buf.getvalue()).decode('utf8')
+    matplotlib.pyplot.close(fig)
+    return plot_url
+
+@app.route('/view-results', methods=['POST'])
+def view_results():
+    results = request.form.get('results')
+    return render_template('view_results.html', results=results)
+
+
+# Route for home page
+@app.route('/', methods=['GET', 'POST'])
+def home():
+    if request.method == 'POST':
+        iterations=1
+        input_array_sizes=None
+        input_array = None
+        #validations
+        execution_type = request.form.get('execution_type')
+
+        if execution_type=='array':
+            if 'array' in request.form and request.form['array']:
+                input_array = list(map(int, request.form['array'].split(',')))
+                provided_input="array provided : "+str(input_array)
+            elif 'array_size' in request.form and request.form['array_size']:
+                size=(int(request.form['array_size']))
+                input_array= generate_random_array(size)
+                provided_input=f"random array generated of length : {len(input_array)}"
+            elif 'file' in request.files and request.files['file']:
+                file = request.files['file']
+                file_content = file.read().decode('utf-8')
+                input_array = list(map(int, file_content.strip().split(',')))
+                provided_input=f"file uploaded containing array of length : {len(input_array)}"
+            else:
+                flash("Please provide an input array or upload a file or choose array_sizes",'danger')
+                return redirect(url_for('home')) 
+        if execution_type=='comparison':#validation for input arrays
+            if 'input_array_sizes' in request.form and request.form['input_array_sizes']:
+                input_array_sizes = list(map(int, request.form['input_array_sizes'].split(',')))
+                input_array_sizes=sorted(input_array_sizes)
+                provided_input="multiple random generated array of sizes "+str(input_array_sizes)
+            else:
+                flash('Please fill the input sizes', 'danger')
+                return redirect(url_for('home')) 
+
+        if 'iterations' in request.form and request.form['iterations']: 
+            iterations=(int(request.form['iterations']))
+        
+        selected_algorithms = request.form.getlist('algorithms')
+
+        algorithms = {
+            'merge_sort': merge_sort,
+            'insertion_sort': insertion_sort,
+            'selection_sort': selection_sort,
+            'bubble_sort': bubble_sort,
+            'quick_sort': quick_sort
+        }
+            # Define input sizes
+        
+        execution_times = {}
+        algorithms_selected={}
+        for name in selected_algorithms:
+            algorithms_selected[name] = algorithms[name]
+        results=""
+        if input_array is not None:
+            results+=f"\n______________________________input array______________________________\n{input_array}\n"
+            for alg_name,alg_func in algorithms_selected.items():
+                avg_time,sorted_arr = measure_execution_time(alg_func, input_array,iterations)
+                execution_times[alg_name] = avg_time
+                results+=f"\n**************** {alg_name}  - execution time :{avg_time} *******************\n{sorted_arr}\n"
+            plot_url=plot_bar_graph(execution_times)
+        elif input_array_sizes is not None:
+            execution_times = {alg_name: [] for alg_name in algorithms_selected.keys()}
+            for size in input_array_sizes:
+                generated_array = generate_random_array(size)
+                results+=f"\n______________________________Generated Array Of Size : {size}______________________________\n{generated_array}\n"
+                for alg_name, alg_func in algorithms_selected.items():
+                    avg_time,sorted_arr = measure_execution_time(alg_func, generated_array,iterations)
+                    execution_times[alg_name].append(avg_time)
+                    results+=f"\n**************** {alg_name} - execution time :{avg_time} *******************\n{sorted_arr}\n"
+                results+="\n\n"
+            # Plotting 
+            plot_url=plot_line_graph(input_array_sizes, execution_times, "Algorithm Performance vs Input Size")
+        return render_template('result.html', plot_url=plot_url, execution_times=execution_times,results=results, input_array=provided_input,iterations=iterations)
+    return render_template('index.html')
+
+
+
+
+def run_flask_app():
+    app.run(host='0.0.0.0',debug=True,)
+
+if __name__ == '__main__':
+    run_flask_app()

app.py

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+import random
+
+def generate_random_array(size):
+    return [str(random.randint(0, 10000)) for _ in range(size)]
+
+array_size = 1000
+dummy_array = generate_random_array(array_size)
+
+
+def write_file(data, file_path):
+    with open(file_path, 'w+') as f:
+        f.write(','.join(data))
+
+write_file(dummy_array, './static/array.txt')

random_array.py

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+blinker==1.8.2
+click==8.1.7
+contourpy==1.2.1
+cycler==0.12.1
+Flask==3.0.3
+fonttools==4.53.1
+itsdangerous==2.2.0
+Jinja2==3.1.4
+kiwisolver==1.4.5
+MarkupSafe==2.1.5
+matplotlib==3.9.1
+numpy==2.0.0
+packaging==24.1
+pillow==10.4.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+six==1.16.0
+Werkzeug==3.0.3

requirements.txt

@@ -0,0 +1 @@
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