Create trendpy2_app.py

trendpy2_app.py

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+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+import trendpy2.models as tpm
+from trendpy2 import __version__
+import numpy as np
+import sympy as sym
+from sympy import atan as arctan
+from sympy import sqrt, sin, cos, tan, exp, log, ln, pi
+a, b, c, x = sym.symbols('a, b, c, x', real=True)
+
+
+
+
+
+def main():
+    st.title(f"TrendPy App")
+
+
+    # Upload CSV file through Streamlit widget
+    uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+
+    if uploaded_file is not None:
+        # Read the CSV file into a DataFrame
+        df = pd.read_csv(uploaded_file, comment='#').select_dtypes(include=np.number)
+
+
+
+        # Allow the user to select two columns for plotting
+        col1, col2 = st.columns(2)  # Use st.columns for horizontal layout
+        selected_x_column = col1.selectbox("Select X-axis column:", df.columns)
+        selected_y_column = col2.selectbox("Select Y-axis column:", df.columns)
+
+        trend_list = ['just raw data',
+                      'linear',
+                      'polynomial',
+                      'trigonometric',
+                      'exponential',
+                      'manual']
+
+        col_trend_1, col_trend_2 = st.columns(2)  # Use st.columns for horizontal layout
+
+        trend = col_trend_1.selectbox('trend', trend_list)
+        if trend == 'polynomial':
+            degree = col_trend_2.selectbox('degree', [1, 2, 3, 4, 5, 6 ])
+
+        if trend == 'manual':
+            ansatz = st.text_input('ansatz')            
+
+        x = df[selected_x_column].to_numpy()
+        y = df[selected_y_column].to_numpy()
+
+        if trend == 'linear':
+            st.latex('f(x)=c_0 x+c_1')
+
+        if trend == 'polynomial':
+            st.latex('f(x)=\sum_{k=0}^n c_{n-k} x^{n-k}')        
+
+        if trend == 'trigonometric':
+            st.latex('f(x)=c_0\cdot\cos(c_1\cdot 2\pi\cdot x+c_2)') 
+
+        if trend == 'exponential':
+            st.latex('f(x)=c_0\cdot \exp(c_1\cdot x)')   
+
+
+        # Create a scatter plot using Plotly Graph Objects
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=x, y=y, mode='markers', name='Data'))
+        fig.update_layout(title_text="Scatter Plot", xaxis_title=selected_x_column, yaxis_title=selected_y_column)
+        if trend == 'linear':
+            fit = tpm.Trend(x, y, 'linReg')
+            r2 = round(fit.r2, 2)
+            x = np.linspace(x[0], x[-1], 500)
+            fig.update_layout(title_text=f"coefs: {fit.coef}")
+            fig.add_trace(go.Scatter(x=x, y=fit.pred(x), mode='lines', name=f'Fit, r2={r2}', marker=dict(color='red')))
+        if trend == 'polynomial':
+            fit = tpm.Trend(x, y, 'polReg', degree)
+            r2 = round(fit.r2, 2)
+            x = np.linspace(x[0], x[-1], 500)
+            fig.update_layout(title_text=f"coefs: {fit.coef}")
+            fig.add_trace(go.Scatter(x=x, y=fit.pred(x), mode='lines', name=f'Fit, r2={r2}', marker=dict(color='red'))) 
+        if trend == 'trigonometric':
+            fit = tpm.Trend(x, y, 'trigReg')
+            r2 = round(fit.r2, 2)
+            x = np.linspace(x[0], x[-1], 500)
+            fig.update_layout(title_text=f"coefs: {fit.coef}")
+            fig.add_trace(go.Scatter(x=x, y=fit.pred(x), mode='lines', name=f'Fit, r2={r2}', marker=dict(color='red')))          
+        if trend == 'exponential':
+            fit = tpm.Trend(x, y, 'expReg')
+            r2 = round(fit.r2, 2)
+            x = np.linspace(x[0], x[-1], 500)
+            fig.update_layout(title_text=f"coefs: {fit.coef}")
+            fig.add_trace(go.Scatter(x=x, y=fit.pred(x), mode='lines', name=f'Fit, r2={r2}', marker=dict(color='red')))       
+        if trend == 'manual':          
+            try:
+                 fit = tpm.Trend(x, y, ansatz='freeReg', freeRegAnsatz=ansatz)
+                 r2 = round(fit.r2, 2)
+                 x = np.linspace(x[0], x[-1], 500)
+                 fig.update_layout(title_text=f"coefs: {fit.coef}")
+                 fig.add_trace(go.Scatter(x=x, y=fit.pred(x), mode='lines', name=f'Fit, r2={r2}', marker=dict(color='red')))              
+            except:
+                print('Enter function expression, like: a*x+b or a*arctan(b*x+c))')
+
+        st.plotly_chart(fig, use_container_width=True)
+
+if __name__ == "__main__":
+    main()