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9198284 d20f8e1 9198284 c7b0c78 9198284 c7b0c78 9198284 c7b0c78 9198284 c7b0c78 9198284 c7b0c78 9198284 c7b0c78 9198284 c7b0c78 d20f8e1 9198284 d286ff3 9198284 c7b0c78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
st.title("Fit Your Data")
default_data = {
"X": [1, 2, 3, 4, 5],
"Y": [2.2, 4.4, 6.5, 8.0, 10.1],
"Select": [True, True, True, True, True]
}
data = pd.DataFrame(default_data)
with st.sidebar:
st.subheader("Enter Your Data")
user_data = st.data_editor(data, num_rows="dynamic", key="data_editor")
xlabel = st.text_input("X label", "X-axis")
ylabel = st.text_input("Y label", "Y-axis")
fit_type = st.radio(
"Choose the Type of Fit",
options=["Logarithmic", "Linear", "Linearithmic", "Quadratic", "Cubic", "Exponential"],
index=0
)
try:
selected_data = user_data[user_data["Select"]]
x = np.array(selected_data["X"], dtype=float)
y = np.array(selected_data["Y"], dtype=float)
if len(x) < 2 and len(y) < 2:
st.warning("Please enter at least 2 data points.")
st.stop()
except ValueError:
st.error("Invalid data entered. Please ensure all values are numeric.")
st.stop()
if fit_type == "Logarithmic":
try:
log_x = np.log(x)
coefficients = np.polyfit(log_x, y , 1)
y_fit = coefficients[0] * log_x + coefficients[1]
r2 = r2_score(y, y_fit)
equation = f"y = {coefficients[0]:.4f}*log(x) + {coefficients[1]:.4f}"
except ValueError:
st.error("Logarithmic fit failed. Ensure all X values are positive.")
st.stop()
elif fit_type == "Linear":
degree = 1
coefficients = np.polyfit(x, y, degree)
y_fit = np.polyval(coefficients, x)
r2 = r2_score(y, y_fit)
equation = f"y = {coefficients[0]:.4f}*x + {coefficients[1]:.4f}"
elif fit_type == "Linearithmic":
try:
x_log_x = x * np.log(x)
A = np.column_stack((x_log_x, x, np.ones_like(x)))
coefficients, _, _, _ = np.linalg.lstsq(A, y, rcond=None)
a, b, c = coefficients
y_fit = a * x_log_x + b * x + c
r2 = r2_score(y, y_fit)
equation = f"y = {a:.4f}*x*log(x) + {b:.4f}*x + {c:.4f}"
except ValueError:
st.error("Linearithmic fir failed. Ensure all X values are positive.")
st.stop()
elif fit_type == "Quadratic":
degree = 2
coefficients = np.polyfit(x, y, degree)
y_fit = np.polyval(coefficients, x)
r2 = r2_score(y, y_fit)
equation = f"y = {coefficients[0]:.4f}*x² + {coefficients[1]:.4f}*x + {coefficients[2]:.4f}"
elif fit_type == "Cubic":
degree = 3
coefficients = np.polyfit(x, y, degree)
y_fit = np.polyval(coefficients, x)
r2 = r2_score(y, y_fit)
equation = f"y = {coefficients[0]:.4f}*x³ + {coefficients[1]:.4f}*x² + {coefficients[2]:.4f}*x + {coefficients[3]:.4f}"
elif fit_type == "Exponential":
try:
log_y = np.log(y)
coefficients = np.polyfit(x, log_y, 1)
a = np.exp(coefficients[1])
b = coefficients[0]
y_fit = a * np.exp(b * x)
r2 = r2_score(y, y_fit)
equation = f"y = {a:.4f}*exp({b:.4f}*x)"
except ValueError:
st.error("Exponential fit failed. Ensure all Y values are positive.")
st.stop()
x_smooth = np.linspace(min(x), max(x), 500)
if fit_type == "Logarithmic":
y_smooth = coefficients[0] * np.log(x_smooth) + coefficients[1]
elif fit_type == "Linearithmic":
y_smooth = a * x_smooth * np.log(x_smooth) + b * x_smooth + c
elif fit_type == "Exponential":
y_smooth = a * np.exp(b * x_smooth)
else:
y_smooth = np.polyval(coefficients, x_smooth)
fig, ax = plt.subplots()
ax.scatter(x, y, color="red", label="Original Data")
ax.plot(x_smooth, y_smooth, color="blue", label=f"{fit_type} Fit (R²={r2:.4f})")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.legend()
ax.set_title(equation)
st.pyplot(fig)
st.write(f"**Fitted Equation**: {equation}")
st.write(f"**R² Value**: {r2:.6f}") |