Create app.py

app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+import random
+import numpy as np
+
+def simple_random_sampling(population_size, sample_size, seed=None):
+    if seed is not None:
+        random.seed(seed)
+    population = list(range(1, population_size + 1))
+    if sample_size > population_size:
+        st.error("Sample size cannot be greater than population size.")
+        return None
+    return random.sample(population, sample_size)
+
+def stratified_sampling(population_size, sample_size, strata_sizes, seed=None):
+    if seed is not None:
+        random.seed(seed)
+    strata_boundaries = np.cumsum(strata_sizes)
+    population = list(range(1, population_size + 1))
+    sample = []
+    
+    for i, size in enumerate(strata_sizes):
+        start = 0 if i == 0 else strata_boundaries[i-1]
+        end = strata_boundaries[i]
+        stratum_population = population[start:end]
+        stratum_sample_size = int(size / population_size * sample_size)
+        sample.extend(random.sample(stratum_population, stratum_sample_size))
+    return sample
+
+def cluster_sampling(clusters, selected_clusters, seed=None):
+    if seed is not None:
+        random.seed(seed)
+    selected = random.sample(clusters, selected_clusters)
+    return [item for sublist in selected for item in sublist]
+
+def systematic_sampling(population_size, sample_size, seed=None):
+    if seed is not None:
+        random.seed(seed)
+    step = population_size // sample_size
+    start = random.randint(0, step - 1)
+    return list(range(start, population_size, step))[:sample_size]
+
+def snowball_sampling(initial_samples, recruit_per_sample, population):
+    sample = set(initial_samples)
+    while len(sample) < recruit_per_sample:
+        recruits = [random.choice(population) for _ in range(recruit_per_sample)]
+        sample.update(recruits)
+    return list(sample)[:recruit_per_sample]
+
+def main():
+    st.title('Advanced Sampling Methods App')
+    st.markdown("""
+        ## Description
+        Select from various sampling methods depending on your research needs.
+        Each method has specific inputs tailored to different research scenarios.
+    """)
+
+    sampling_method = st.sidebar.selectbox("Choose the sampling method", 
+                                           ("Simple Random Sampling", "Stratified Sampling", 
+                                            "Cluster Sampling", "Systematic Sampling", 
+                                            "Snowball Sampling"))
+    
+    population_size = st.sidebar.number_input('Enter Population Size', min_value=1, value=1000, step=1)
+    sample_size = st.sidebar.number_input('Enter Sample Size', min_value=1, value=50, step=1)
+    seed = st.sidebar.number_input('Enter Random Seed (optional)', min_value=0, value=None, step=1, format='%d', key='seed')
+
+    if sampling_method == "Stratified Sampling":
+        strata_sizes = st.sidebar.text_input('Enter Strata Sizes (comma-separated, e.g., 10,20,30)')
+        strata_sizes = list(map(int, strata_sizes.split(','))) if strata_sizes else []
+
+    if sampling_method == "Cluster Sampling":
+        num_clusters = st.sidebar.number_input('Enter Number of Clusters', min_value=1, value=5, step=1)
+        selected_clusters = st.sidebar.number_input('Enter Number of Clusters to Sample', min_value=1, value=2, step=1)
+        clusters = [list(range(i * 100, (i + 1) * 100)) for i in range(num_clusters)]
+
+    if st.sidebar.button('Generate Sample'):
+        sample = []
+        if sampling_method == "Simple Random Sampling":
+            sample = simple_random_sampling(population_size, sample_size, seed)
+        elif sampling_method == "Stratified Sampling":
+            sample = stratified_sampling(population_size, sample_size, strata_sizes, seed)
+        elif sampling_method == "Cluster Sampling":
+            sample = cluster_sampling(clusters, selected_clusters, seed)
+        elif sampling_method == "Systematic Sampling":
+            sample = systematic_sampling(population_size, sample_size, seed)
+        elif sampling_method == "Snowball Sampling":
+            initial_samples = [random.randint(1, population_size) for _ in range(5)]
+            sample = snowball_sampling(initial_samples, sample_size, range(1, population_size + 1))
+        
+        if sample:
+            st.write(f'### {sampling_method} Output')
+            st.write(f'Sample: {sample}')
+            st.write(f'Sample Size: {len(sample)}')
+            if seed is not None:
+                st.write(f'Random Seed Used: {seed}')
+
+if __name__ == '__main__':
+    main()