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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()
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