Merge branch 'master' into docker-merge

Dockerfile

@@ -0,0 +1,10 @@
+FROM python
+
+RUN pip install flask scipy matplotlib
+# COPY chi_square.py kolmogorov_smirnov.py ./
+COPY server.py ./
+COPY templates templates
+RUN mkdir static
+
+# ENTRYPOINT ["python", "chi_square.py"]
+ENTRYPOINT ["flask", "run"]

Makefile

@@ -0,0 +1,8 @@
+run: build
+	docker compose up
+
+build:
+	docker compose build
+
+push:
+	docker compose push

chi_square.py

@@ -104,11 +104,11 @@ def index():
         plt.savefig('static/plot.png')
 
         # Render template with plot image and results
-        return render_template('home.html', plot_path='static/plot.png', chisq_stat=chisq_stat, p_value=p_value)
+        return render_template('chi_square.html', plot_path='static/plot.png', chisq_stat=chisq_stat, p_value=p_value)
 
     else:
         # Render initial form
-        return render_template('home.html')
+        return render_template('chi_square.html')
 
 if __name__ == '__main__':
     app.run(debug=True)

docker-compose.yml

@@ -0,0 +1,13 @@
+version: "3.9"
+services:
+  analytics_learning:
+    image: marcpartensky/analytics_learning
+    build: .
+    ports:
+      - "5000:5000"
+    environment:
+      FLASK_APP: server.py
+      # FLASK_APP: kolmogorov_smirnov.py
+      # FLASK_APP: chi_square.py
+      FLASK_RUN_HOST: "0.0.0.0"
+      FLASK_RUN_PORT: 5000

kolmogorov_smirnov.py

@@ -103,11 +103,11 @@ def index():
             plt.savefig('static/plot.png')
 
             # Render template with plot
-            return render_template('index.html', plot_url='static/plot.png')
+            return render_template('kolmogorov_smirnov.html', plot_url='static/plot.png')
         else:
-            return render_template('index.html', error=True)
+            return render_template('kolmogorov_smirnov.html', error=True)
     else:
-        return render_template('index.html')
+        return render_template('kolmogorov_smirnov.html')
 
 if __name__ == '__main__':
     app.run(debug=True, port=8000)

server.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+from flask import Flask, render_template, request
+import numpy as np
+import scipy.stats as stats
+from scipy.stats import expon, gamma, norm, poisson
+import matplotlib.pyplot as plt
+
+app = Flask(__name__)
+
+# Define routes
+@app.route('/chi_square', methods=['GET', 'POST'])
+def chi_square():
+    if request.method == 'POST':
+        # Get distribution type from form submission
+        distribution_type = request.form['distribution_type']
+
+        # Generate random sample data for selected distribution
+        if distribution_type == 'normal':
+            data = np.random.normal(loc=0, scale=1, size=100)
+        elif distribution_type == 'exponential':
+            data = np.random.exponential(scale=1, size=100)
+        elif distribution_type == 'poisson':
+            data = np.random.poisson(lam=1, size=100)
+        elif distribution_type == 'gamma':
+            data = np.random.gamma(shape=2, scale=1, size=100)
+
+        # Calculate expected values for selected distribution
+        if distribution_type == 'normal':
+            expected = stats.norm(loc=np.mean(data), scale=np.std(data)).pdf(data)
+        elif distribution_type == 'exponential':
+            expected = stats.expon(scale=np.mean(data)).pdf(data)
+        elif distribution_type == 'poisson':
+            expected = stats.poisson(mu=np.mean(data)).pmf(data)
+        elif distribution_type == 'gamma':
+            expected = stats.gamma(a=np.mean(data)**2/np.var(data), scale=np.var(data)/np.mean(data)).pdf(data)
+
+        # Calculate Chi-square statistic and p-value
+        chisq_stat, p_value = stats.chisquare(f_obs=np.histogram(data, bins='auto')[0], f_exp=expected)
+
+        # Create histogram of sample data and plot expected distribution
+        plt.hist(data, bins='auto', density=True, alpha=0.7)
+        plt.plot(data, expected, 'r-', lw=2)
+        plt.title(f'{distribution_type.capitalize()} Distribution')
+        plt.xlabel('Value')
+        plt.ylabel('Density')
+        plt.savefig('static/plot.png')
+
+        # Render template with plot image and results
+        return render_template('chi_square.html', plot_path='static/plot.png', chisq_stat=chisq_stat, p_value=p_value)
+
+    else:
+        # Render initial form
+        return render_template('chi_square.html')
+
+@app.route('/kolmogorov_smirnov', methods=['GET', 'POST'])
+def kolmogorov_smirnov():
+    if request.method == 'POST':
+        # Get user input
+        distribution_type = request.form['distribution_type']
+        num_samples = int(request.form['num_samples'])
+
+        # Generate data
+        if distribution_type == 'normal':
+            data = norm.rvs(size=num_samples)
+        elif distribution_type == 'uniform':
+            data = np.random.uniform(size=num_samples)
+        elif distribution_type == 'standard':
+            data = np.random.randn(num_samples)
+        elif distribution_type == 'exponential':
+            data = expon.rvs(size=num_samples)
+        elif distribution_type == 'poisson':
+            data = poisson.rvs(mu=5, size=num_samples)
+        elif distribution_type == 'gamma':
+            data = gamma.rvs(a=5, size=num_samples)
+        else:
+            data = None
+
+        # Generate plot
+        if data is not None:
+            plt.hist(data, bins=30)
+            plt.title(f'{distribution_type.capitalize()} Distribution')
+            plt.xlabel('Value')
+            plt.ylabel('Frequency')
+            plt.savefig('static/plot.png')
+
+            # Render template with plot
+            return render_template('kolmogorov_smirnov.html', plot_url='static/plot.png')
+        else:
+            return render_template('kolmogorov_smirnov.html', error=True)
+    else:
+        return render_template('kolmogorov_smirnov.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)