NanoDotOptimization/plot.py at main · PatrickSJacobs/NanoDotOptimization · GitHub

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from matplotlib import pyplot
import statistics
from scipy.signal import find_peaks
from pathlib import Path
from matplotlib.backends.backend_pdf import PdfPages

logshift = lambda x: np.log(x + 1)

def list_files_in_directory(directory):
    # Create a Path object for the directory
    path = Path(directory)

    # List all files in the directory
    return [str(file) for file in path.iterdir() if file.is_file()]

directory_path = '/Users/calaeuscaelum/Documents/Development/Tang_Project/NanoDotOptimization/data/possible_candidates/opt_10_14_2024__10_56_54_raw/'
files = list_files_in_directory(directory_path)


# Create a PdfPages object
with PdfPages('data/Best_NanoDot_Plots.pdf') as pdf:
    for fi in range(len(files)):
        file = files[fi]
        # define the true objective function
        scaling_factor = 10000

        sr = float(file.split('_sr_')[1].split('nm_')[0].replace("_", ".")) / scaling_factor
        ht = float(file.split('_ht_')[1].split('nm_')[0].replace("_", ".")) / scaling_factor
        cs = 0.4 - 2 * sr
        try:
            cs_split = file.split('_cs_')[1]
            cs = float(cs_split.split('nm_')[0].replace("_", ".")) / scaling_factor
        except IndexError:
            pass
        theta_deg = float(file.split('_deg_')[1].split('.csv')[0].replace("_", ".")) % 360

        data = pd.read_csv(file)

        K = 2

        xs = data["wvl"].tolist()
        ys = data["refl"].tolist()

        xs = xs[: len(xs) - K]
        ys = ys[: len(ys) - K]

        plt.plot(xs, ys, color = 'grey' )

        plt.xlabel('wavelength')
        plt.ylabel('reflectance')

        mam = max(ys)
        ind = []
        maxis = []
        ty = len(ys)
        for i in range(ty):
            j = ys[i]
            if j >= 0.5 * mam:
                maxis += [j]
                ind += [i]

        ind = [i for i in range(min(ind), max(ind)+1)]
        maxis = [ys[i] for i in ind]

        neg_ys_prime = [element + mam for element in [(-1)*y for y in maxis]]
        neg_ys = [element + mam for element in [(-1)*y for y in ys]]

        peaks, _ = find_peaks(neg_ys_prime)
        maxi = 0

        if len(peaks) > 0:
            neg_min = mam
            real_min = 0

            for peak in peaks:
                if neg_min >= neg_ys[ind[peak]]:
                    neg_min = neg_ys[ind[peak]]
                    real_min = ys[ind[peak]]

            index_list = [i for i in range(len(ys)) if ys[i] >= real_min]

            ys_fixed = [ys[i] for i in index_list]

            L = []
            for (wvl, freq) in zip([xs[i] for i in index_list], np.ceil(np.array(ys_fixed) / np.min(ys_fixed))):
                L += [wvl for i in range(int(freq))]

            maxi = statistics.mean(L)

        else:
            maxi = xs[ys.index(mam)]

        #tsr = sum(x * i for i, x in enumerate(L, 1)) / len(L)

        #print(tsr)

        q = 1000

            # 1/(1 + (q/b*(x - a))^2)/c == 1/(pc)

        def objective(x, b, c):
            # maxi = np.array([xs[ys.index(mam)] for i in range(len(x))])
            # maxi = sum(u * i for i, u in enumerate(x, 1)) / len(x)
            maximum = np.array([maxi for i in x])

            #f = q / (1 + (q / b * (x - maximum)) ** 2) / c
            f = 1 / (c**2*(1 + (q / b * (x - maximum)) ** 2))

            return f

        popt, popv = curve_fit(objective, xs, ys)

        b, c = popt
        b_var = popv[0][0]
        c_var = popv[1][1]

        #print((b, c - 1000))

        # calculate the output for the range
        y_line = objective(xs, b, c)
        # create a line plot for the mapping function
        u = [xs[ind[i]] for i in peaks]
        p = [ys[ind[i]] for i in peaks]
        #u = [xs[i] for i in ind]
        #p = [ys[i] for i in ind]
        plt.plot(u, p, "x")
        plt.plot(xs, y_line, '--', color='red')
        plt.title(f"sr = {sr*1000:.4f} nm, ht = {ht*1000:.4f} nm, cs = {cs*1000:.4f} nm")

        #plt.ylim(0, 1)
        #plt.suptitle(f"b = {logshift(abs(b)):.2f}, c = {logshift(abs(c**2 * 10 - 10)):.2f}, b_var = {logshift(abs(b_var * 100)):.2f}, c_var = {logshift(abs(c_var * 100)):.2f}")
        #plt.suptitle(f"#{fi+1}: b = {abs(b):.2f}, c = {abs(c**2 * 10 - 10):.2f}, b_var = {abs(b_var * 100):.2f}, c_var = {abs(c_var * 100):.2f}, magn = {(abs(b)**2 + abs(c**2 * 10 - 10)**2 + abs(b_var * 100)**2)**(1/2):.2f}")
        plt.suptitle(f"#{fi+1}: b = {abs(b):.2f}, c = {abs(c**2 * 10 - 10):.2f}, b_var = {abs(b_var * 100):.2f}, c_var = {abs(c_var * 100):.2f}")

    # Add the second subtitle using fig.text()
        #plt.show()
        pdf.savefig()
        plt.clf()
        plt.close()