Copied code from theory repository

.gitignore.gitignore

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+# ignore
+*.mp4
+*.dll
+*.npz
+*.pyc
+*.pyc
+*.pdf
+*.svg
+*.npz
+*.gds
+*.h5
+*.png

figures/.vscode/settings.json

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+{
+    "python.analysis.typeCheckingMode": "basic"
+}

figures/FrequencyShifting/FSM653.py

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+import sys,os
+import glob
+sys.path.append('D:\\GitHub\\QuantumOpticsTheory\\CollectiveDecay\\Python\\CollectiveDecay')
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import cm
+from matplotlib.colors import ListedColormap, LinearSegmentedColormap
+from scipy.optimize import curve_fit
+from scipy.interpolate import interp1d
+
+import Transformations
+
+save_path = r'C:\Users\chris\Dropbox\Apps\Overleaf\Superradiance and subradiance in light shifted organic molecules\Figures\FrequencyShifting' + '\\'
+
+### Finding files ###
+data_path = r'D:\QOExperimentData\FrequencyShiftMeasurement\measurement0653'
+probes = sorted(glob.glob(data_path + "\\probe*"))
+
+
+my_cmap = cm.get_cmap('gist_heat_r', 512)(np.linspace(0, 1, 512))
+my_cmap[0:16,:] = np.array([1,1,1,1])
+my_cmap = ListedColormap(my_cmap)
+
+
+### Tuning trace plot ### 
+def get_scan_data(probe, rolling_avg=100): 
+    res_path = probe + '\\RESONANCES.npz'
+    if not os.path.exists(res_path): return None, None
+    data = np.load(res_path, allow_pickle=True)
+    f = data['frequencies']
+    c = data['counts']
+    f = Transformations.match_arrays(f, c)
+    f = Transformations.rolling_average(f, rolling_avg)
+    c = Transformations.rolling_average(c, rolling_avg)
+    return f, c
+
+interp_fun_list = []
+for i,p in enumerate(probes):
+    if i in range(0, 500):
+        f, c = get_scan_data(p)
+        if f is None: continue
+        interp_fun_list.append(interp1d(f, c, bounds_error=False, fill_value=0))
+
+c = 3e8 
+fmin, fmax = 382620, 382680
+fmin, fmax = 382400, 382800
+fmin, fmax = 382460, 382610
+fmin, fmax = c / 784.4, c / 784
+
+f_list = np.linspace(fmin, fmax, 1000)
+l_list = np.linspace(c / fmax, c / fmin, 1000)
+f_list = c / l_list
+
+c_array = []
+for interp_fun in np.flip(interp_fun_list):
+    c_array.append(interp_fun(f_list))
+c_array = np.flip(np.transpose(c_array))
+c_array /= np.max(c_array)
+c_array[np.where(c_array > 0.1)] = 1
+
+# extent = [0, c_array.shape[1], fmin - np.mean([fmin, fmax]), fmax - np.mean([fmin, fmax])]
+extent = [0, c_array.shape[1], c / fmax, c / fmin]
+
+fig, ax = plt.subplots(figsize=(175 / 72 * 4 / 3, 175 / 72))
+# fig, ax = plt.subplots()
+plt.rcParams.update({'font.size': 8, 'pdf.fonttype': 42, "font.family": "Arial"})
+
+plt.imshow(c_array, aspect='auto', extent=extent, cmap=my_cmap)
+plt.xlabel("Probe number")
+plt.ylabel("Wavelength (nm)")
+plt.yticks([784, 784.1, 784.2, 784.3, 784.4])
+
+plt.tight_layout()

figures/FrequencyShifting/Transformations.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 20 09:21:44 2023
+
+@author: chris
+"""
+import numpy as np
+
+def match_arrays(x, y):
+    return np.interp(np.linspace(0,len(x),len(y)), range(0,len(x)-1),x[0:-1])
+
+def resize(x, n):
+    return np.interp(np.linspace(0, len(x), n), range(0,len(x)-1),x[0:-1])
+
+def fill_evenly(data, locations, size):
+    assert len(data) == len(locations), 'Data and locations must be of the same length'
+    assert size > 0, 'Size should be a positive integer'
+    assert locations[-1] <= 1, 'Last location should not exceed 1'
+    locations[0] = 0
+
+    idx = [int(np.floor(loc*size)) for loc in locations]
+    output = np.array([data[0]] * size)
+    for i,_ in enumerate(idx):
+        if i + 1 < len(idx):
+            output[idx[i]:idx[i+1]] = data[i]
+        else:
+            output[idx[i]:output.size] = data[i]
+
+    return output
+
+
+def rolling_average(x, n):
+    pad_width = n // 2
+    x = np.pad(x, pad_width, mode='edge')
+    window = np.ones(n) / n
+    return np.convolve(x, window,'valid')
+
+
+if __name__=="__main__":
+    output = fill_evenly([1,5,7], [0,0.5,0.8], 10)
+    print(output)