This Jupyter Notebook demonstrates the application of the Fast Dynamic Time Warping (FastDTW) algorithm to reconcile and compare astronomical data, specifically focusing on magnitude and color shifts between fiducial lines and isochrones. It includes data loading, preprocessing, and shifting color and magnitude values to find minimal differences, utilizing libraries such as matplotlib for plotting and scipy for calculations. The notebook provides a detailed example of using FastDTW to understand and visualize the alignment and differences in astronomical observations.

snippet.python

import pickle as pkl
import matplotlib.pyplot as plt
from fastdtw import fastdtw
from scipy.spatial.distance import euclidean, hamming
import numpy as np
from mplEasyAnimate import animation
from tqdm import tqdm
import similaritymeasures
from scipy.interpolate import interp1d

snippet.python

PATH = "../DebugOutput.pkl"

snippet.python

with open(PATH, 'rb') as f:
    debugOutput = pkl.load(f)
isoColor, isoMag, fFid, fiducialLine = debugOutput
corrLists = list()
 
nMin = 5
nMax = 25
initShift = np.mean(fiducialLine[1]) - np.mean(isoMag)
isoMagOrig = isoMag.copy()
isoMag = isoMag + initShift
 
f = interp1d(isoMag, isoColor, bounds_error=False, fill_value='extrapolate')
for n in tqdm(range(nMin, nMax), desc="Guessing mu"):
    correlations = list()
    magShifts = np.linspace(-10,10, n)
    fMagEval = np.linspace(fiducialLine[1].min(), fiducialLine[1].max(), n)
    iMagEval = np.linspace(isoMag.min(), isoMag.max(), n)
    dtwX = np.zeros(shape=(n,1))
    dtwY = np.zeros(shape=(n,1))
    fColor = fFid(fMagEval)
    for magShift in magShifts: 
        iColor = f(iMagEval+magShift)
 
 
        dtwX[:, 0] = fColor
        dtwY[:, 0] = iColor
        dtw = fastdtw(dtwX, dtwY, dist=euclidean)
 
        minFiducialColor, maxFiducialColor = min(fMagEval), max(fMagEval)
        dists = list()
        for i, j in dtw[1]:
            if minFiducialColor <= iMagEval[j] <= maxFiducialColor:
                dists.append(np.sqrt((fColor[i] - iColor[j])**2 + (fMagEval[i] - iMagEval[j])**2))
        chi2 = np.sum([x**2 for x in dists])
        chi2nu = chi2/len(dists)
        correlations.append(chi2nu)
    magShift = magShifts[np.argmin(correlations)]
    corrLists.append(magShift)
magShift = np.mean(corrLists)
print(magShift)
 
n = 100
with plt.style.context(pubStyle):
    fig, axs = plt.subplots(1,2,figsize=(20,7))
    ax = axs[0]
    fMagEval = np.linspace(fiducialLine[1].min(), fiducialLine[1].max(), n)
    iMagEval = np.linspace(isoMag.min(), isoMag.max(), n)
 
    fColor = fFid(fMagEval)
    isoMagShifted = isoMag - magShift
    isoColorShifted = isoColor
    isoFuncShifted = interp1d(isoMagShifted, isoColorShifted, bounds_error=False, fill_value='extrapolate')
    iColor = isoFuncShifted(fMagEval)
 
    ax.plot(fColor, fMagEval, 'o-', markersize=3)
    ax.plot(iColor, fMagEval, 'o-', markersize=3)
 
    dtwX = np.zeros(shape=(n,1))
    dtwY = np.zeros(shape=(n,1))
 
    dtwX[:, 0] = fColor
    dtwY[:, 0] = iColor
    dtw = fastdtw(dtwX, dtwY, dist=euclidean)
    minFiducialColor, maxFiducialColor = min(fMagEval), max(fMagEval)
    dists = list()
    for i, j in dtw[1]:
        # if minFiducialColor <= iMagEval[j] <= maxFiducialColor:
        if ((j < len(iColor)-1) and j != 0) and ((i < len(fColor)-1) and i != 0):
            dists.append(np.sqrt((fColor[i] - iColor[j])**2 + (fMagEval[i] - fMagEval[j])**2))
            ax.plot([fColor[i], iColor[j]], [fMagEval[i], fMagEval[j]], color='black', alpha=0.1)
    chi2 = np.sum([x**2 for x in dists])
    chi2nu = chi2/len(dists)
    ax.invert_yaxis()
 
    axs[1].plot(fiducialLine[0], fiducialLine[1], label="Fiducial Line")
    axs[1].plot(isoColor, isoMagOrig, label="Isochrone")
    axs[1].legend(frameon=False)
    axs[1].invert_yaxis()
 
    axs[1].set_facecolor("#F0F1EB")
    axs[0].set_facecolor("#F0F1EB")
    fig.set_facecolor("#F0F1EB")
 
    axs[0].set_xlabel("F275W - F814W", fontsize=25)
    axs[1].set_xlabel("F275W - F814W", fontsize=25)
    axs[0].set_ylabel("F814W", fontsize=25)
    axs[1].set_ylabel("F814W", fontsize=25)
 
    fig.savefig("DTWChi2.png", dpi=200)
 
    # axs[1].set_xlim(ax.get_xlim())
    # axs[1].set_ylim(ax.get_ylim())

Guessing mu: 100%|███████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.15it/s]


1.0163341752771688

snippet.python

magShift

-30.0

snippet.python

colorShift

0.7366746325569853

snippet.python

list(range(5, 25))

[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]

python