pukpr

import numpy as np
from sklearn.linear_model import Lasso

# ----------------------------
# USER INPUT: hidden layer & target
# ----------------------------
# Example synthetic data (replace with your arrays)
N = 1000
t = np.linspace(0, 10, N)
hidden_layer = np.sin(2*np.pi*1*t) + 0.5*np.sin(2*np.pi*3*t)  # latent forcing

pukpr

#!/usr/bin/env python3
"""
timeseries_modeler.py

Read a two-column CSV (time, value). Read a JSON file with the same filename
appended by ".p" (e.g. data.csv.p) into a data dictionary. Create a clone of the
time-series and run a loop that steps through timestamps, calling a user-
customizable model-step function to produce a modeled series. At the end the
script computes Pearson's correlation coefficient and the variance of the
squared errors (and also MSE), using library routines.

pukpr

#!/usr/bin/env python3
"""
timeseries_modeler.py

Read a two-column CSV (time, value). Read a JSON file with the same filename
appended by ".p" (e.g. data.csv.p) into a data dictionary. Create a clone of the
time-series and run a loop that steps through timestamps, calling a user-
customizable model-step function to produce a modeled series. At the end the
script computes Pearson's correlation coefficient and the variance of the
squared errors (and also MSE), using library routines.

pukpr

{"PeriodsPhase":[13.599781479999999,7.510001926736354,18.679732370000025,7.195787246118572,20.998850680000004,9.392972953999996,6.001334823,10.345732619999998,9.26752208,6.276528506318169,6.158543069000001,15.797059490000018,5.047887753499271,7.347247053426061,1.7632983608814414,6.514613283064518,7.845674634],"Hold":0.0015847482104409999,"Initial":0.019102685632566375,"Imp_Stride":2,"LTE_Freq":182.73199150000377,"Periods":[27.2122,27.3216,27.5545,13.63339513,13.69114014,13.6061,13.6608,13.71877789,6795.985773,1616.215172,2120.513853,13.77725,3232.430344,9.132931547,9.108450374,9.120674533,27.0926041],"LTE_Amp":1.2282362293246383,"DeltaTime":0.0,"Imp_Amp":38.41771511999972,"AliasedPhase":[12.936492162500015,10.819234671777158,17.28825828537955,9.83624739277385,24.275678314901782,15.228945466500011,8.477116474739738,14.315824278046344,9.824004490952355,6.960279582531864,4.2964023424706665,19.297607654892147,3.967560759570126,22.113385778146025,8.444613035937136,4.885017823159738,10.475305999774953,6.49431729103

pukpr

pukpr

from vpython import *

Running = True

def exit_app(evt):
    global Running
    if evt.key == 'q':  # Press 'q' to quit
        print("Exiting simulation...")
        Running = False

pukpr

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import periodogram, windows

# Parameters
years = 500
dt = 1 / 365.25  # daily resolution
t = np.arange(0, years, dt)
fs = 1 / dt  # sampling frequency in cycles per year

pukpr

with Text_IO;
with GEM.LTE.Primitives;

function GEM.Mix_Regression (File_Name : in String) return Gem.LTE.Period_Set is
   use GEM.LTE, GEM.LTE.Primitives;
   D : Data_Pairs := Make_Data(File_Name);
   First, Last : Integer;
   Singular : Boolean;
   Forcing : Data_Pairs := D;
   Model : Data_Pairs := D;

pukpr

   function Tide_Sum_Custom (Template : in Data_Pairs;
                      Constituents : in Long_Periods_Amp_Phase;
                      Periods : in Long_Periods;
                      Ref_Time : in Long_Float := 0.0;
                      Scaling : in Long_Float := 1.0;
                      Cos_Phase : in Boolean := True;
                      Year_Len : in Long_Float := Year_Length;
                      Integ: in Long_Float := 0.0;
                      Ext_Forcing : in Data_Pairs := Empty_Data;
                      Ext_Factor  : in Long_Float := 0.0;

pukpr

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def load_signal(csv_path):
    df = pd.read_csv(csv_path)
    time = df.iloc[:, 0].values
    signal = df.iloc[:, 1].values
    return time, signal