import streamlit as st
import matplotlib.pyplot as plt
import numpy as np


st.header("Normal distribution")

col_plot, col_settings = st.columns(2)
MU_DEFAULT, SIGMA_DEFAULT, SAMPLES_DEFAULT = 0.0, 1.0, 1000

if st.button("Set default"):
    st.session_state.mu = MU_DEFAULT
    st.session_state.sigma = SIGMA_DEFAULT
    st.session_state.samples = SAMPLES_DEFAULT

if st.button("Max samples"):
    st.session_state.samples = 100_000

with col_settings:
    st.slider("Distribution mean", -100.0, 100.0, MU_DEFAULT, key="mu")
    st.slider("Distribution standard deviation", 0.01, 100.0, SIGMA_DEFAULT, key="sigma")
    st.number_input("Distribution samples", 0, 100_000, SAMPLES_DEFAULT, key="samples")

dist = np.random.normal(st.session_state.mu,
                        st.session_state.sigma,
                        st.session_state.samples)

with col_plot:
    fig, ax = plt.subplots()

    counts, bins, _ = ax.hist(dist, 30, density=True)
    ax.plot(bins, 1.0 / (st.session_state.sigma * np.sqrt(2.0 * np.pi)) * np.exp( - (bins - st.session_state.mu) ** 2.0 / (2.0 * st.session_state.sigma ** 2.0)), linewidth=2, color='r')
    st.pyplot(fig)