Thankyou @sjahangiri

This helps a lot. I have reshaped the matrices to fit. The modes were given as (9,15), but I have taken the transpose of this. However I am still getting the error message

```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[11], line 124
116 wi = np.array([1.24800e+01, 1.21200e+01, 7.51000e+00, 0.00000e+00, 3.00000e-02,
117 4.00000e-02, 1.67567e+03, 1.67576e+03, 1.67577e+03, 1.90370e+03,
118 1.90373e+03, 3.52600e+03, 3.78663e+03, 3.78675e+03, 3.78678e+03])
120 wf = np.array([9.58000e+00, 5.22000e+00, 4.88000e+00, 4.70000e+00, 2.00000e-02,
121 1.30000e-01, 2.10000e-01, 1.90700e+01, 2.37100e+01, 8.65110e+02,
122 1.71559e+03, 1.71581e+03, 3.55823e+03, 3.82900e+03, 3.82936e+03])
--> 124 Ud, delta = qchem.duschinsky(Li, Lf, ri, rf, wf, m)
126 plt.imshow(abs(Ud), cmap="Greens")
127 plt.colorbar()
File ~\anaconda3\Lib\site-packages\strawberryfields\apps\qchem\utils.py:93, in duschinsky(Li, Lf, ri, rf, wf, m)
28 r"""Generate the Duschinsky rotation matrix :math:`U` and displacement vector :math:`\delta`.
29
30 The Duschinsky transformation relates the normal coordinates of the initial and
(...)
89 :math:`\delta`
90 """
91 U = Lf.T @ Li
---> 93 d = Lf.T * m**0.5 @ (ri - rf)
95 l0_inv = np.diag((h / (wf * 100.0 * c)) ** (-0.5) * 2.0 * pi) / 1.0e10 * m_u**0.5
97 delta = np.array(d @ l0_inv)
ValueError: operands could not be broadcast together with shapes (9,15) (15,1)
```

But I do not understand, as I have reshaped all the arrays, see below;

Here is my code. I have inserted the data manually rather than using the `read_gamess`

function.

```
import numpy as np
import strawberryfields as sf
from strawberryfields.apps import qchem
import matplotlib.pyplot as plt
# r = coords, m = masses, w = freq, L = modes
m = np.array([12. , 1.00782, 1.00782, 1.00782, 1.00782,12. , 1.00782, 1.00782, 1.00782, 1.00782,12. , 1.00782, 1.00782, 1.00782, 1.00782])
m = np.reshape(m, (m.shape[0], 1))
ri = np.array([-1.34848393e-01, 1.45102586e+00, -7.78600000e-07,
9.48151120e-01, 1.45094531e+00, -2.18030000e-05,
-4.95844774e-01, 2.12550214e+00, -7.66575787e-01,
-4.95881634e-01, 1.77756817e+00, 9.67422229e-01,
-4.95826320e-01, 4.49888516e-01, -2.00823860e-01])
rf = np.array ([0.18911291, 2.34712294, 0.18027572,
1.12702634, 1.82103898, 0.07528684,
-0.37809845, 2.51594969, -0.72380278,
-0.3765942 , 2.157796 , 1.08122229,
-1.2356966 , -1.58697761, -0.61298207])
Li = np.array([[-5.7550730e-02, -9.6378750e-02, 1.0274670e-02, 6.7000000e-06,
-1.8810000e-05, 7.1000000e-07, -4.2092350e-02, -7.8580440e-02,
-1.7383430e-02],
[-8.4813860e-02, 5.5856730e-02, 4.8922830e-02, -1.1420000e-05,
7.2300000e-06, 1.9520000e-05, -7.5951850e-02, 3.2305320e-02,
3.7897600e-02],
[-4.6922800e-02, 1.7246500e-02, -1.0103026e-01, 8.6500000e-06,
1.1950000e-05, -3.3000000e-07, -2.6602530e-02, 3.2102340e-02,
-8.0689500e-02],
[-5.4276260e-02, -9.0969330e-02, 9.6846400e-03, 1.6900000e-05,
-3.9380000e-05, 4.9805072e-01, 3.7957733e-01, 7.0841836e-01,
1.5669270e-01],
[ 4.1870525e-01, -2.7563321e-01, -2.4155155e-01, 1.7597873e-01,
-4.6598604e-01, -2.5080000e-05, -3.2751400e-03, 1.3428900e-03,
1.6125100e-03],
[ 2.3164028e-01, -8.5139940e-02, 4.9885581e-01, 4.6590732e-01,
1.7586280e-01, -2.2100000e-06, -1.1425900e-03, 1.3639200e-03,
-3.4493000e-03],
[ 2.8494248e-01, 3.6864020e-01, -2.4397831e-01, -2.2023407e-01,
-4.1462052e-01, -1.6599249e-01, -4.5435940e-02, 6.7879840e-02,
2.6047695e-01],
[ 4.1715417e-01, -7.5456950e-02, 1.1938824e-01, -3.8167052e-01,
7.8489720e-02, 3.1013301e-01, 7.8275000e-02, -1.3172194e-01,
-4.8643895e-01],
[ 2.3341572e-01, -3.1292895e-01, 8.8542380e-02, -2.3208301e-01,
2.6431321e-01, -3.5247987e-01, -9.3794910e-02, 1.5264852e-01,
5.5124794e-01],
[ 3.7879001e-01, 3.4966671e-01, 1.0403589e-01, 4.6923181e-01,
1.6677270e-02, -1.6601344e-01, -1.0038778e-01, 1.9175435e-01,
-1.6636720e-01],
[ 3.3304536e-01, -1.6169095e-01, -3.8143984e-01, 3.6423050e-02,
4.7347115e-01, 1.5017193e-01, 8.5921640e-02, -1.7510752e-01,
1.5143653e-01],
[-2.2094120e-02, 2.5307597e-01, 8.4205880e-02, 1.6281085e-01,
-1.5358352e-01, 4.4487181e-01, 2.6303948e-01, -5.2146573e-01,
4.4037443e-01],
[ 7.5790670e-02, 5.2022804e-01, 7.9190600e-03, -2.4909445e-01,
3.9820662e-01, -1.6605323e-01, 2.6743282e-01, -3.2408650e-02,
-4.3820950e-02],
[-1.5904029e-01, -1.5229553e-01, -7.8912780e-02, 1.6940475e-01,
-8.6060900e-02, -4.6051208e-01, 7.4342420e-01, -7.9167300e-02,
-1.1785036e-01],
[ 1.1574001e-01, -6.0358260e-02, 5.3134631e-01, -3.9673811e-01,
-2.8673472e-01, -9.2385800e-02, 1.4864984e-01, -1.4783800e-02,
-2.7417010e-02]])
Lf = np.array([[ 2.7984740e-02, 1.2029700e-02, -2.1681160e-02, -4.2138570e-02,
2.9407500e-02, -7.2854000e-02, 7.1841200e-03, 2.4478200e-03,
-8.9376960e-02],
[-5.0101500e-03, -9.7562700e-03, -5.9721690e-02, -1.1824614e-01,
4.9165700e-03, 3.1043620e-02, 1.9920510e-02, 1.7565100e-02,
3.1184390e-02],
[-2.3819040e-02, 2.3526110e-02, -1.2121640e-02, -2.3433470e-02,
-7.7712070e-02, -2.5602210e-02, 3.9579700e-03, -9.3046850e-02,
3.5288400e-03],
[-3.1015851e-01, -1.6636281e-01, -2.0597010e-02, 2.5743664e-01,
5.1614910e-02, -1.2619182e-01, 5.0655275e-01, -1.8757240e-02,
6.6736628e-01],
[-6.2183044e-01, -3.1179174e-01, -5.7887230e-02, 4.3796489e-01,
-1.0740452e-01, -1.3004910e-01, -2.6243299e-01, 1.1210910e-02,
-3.7352556e-01],
[ 4.5869200e-02, -5.6777600e-02, -1.1619380e-02, 8.6733060e-02,
6.9910964e-01, 2.6575130e-01, -5.2421660e-02, -1.4818600e-03,
-7.4667720e-02],
[ 1.0512270e-02, 2.9484893e-01, -2.2763060e-02, 1.2204827e-01,
-4.9578676e-01, 3.7721102e-01, -2.9649757e-01, 3.4480243e-01,
2.0855672e-01],
[ 7.5235200e-03, 7.3556686e-01, -6.3105700e-02, 5.0102207e-01,
2.0031011e-01, 2.9899650e-02, 1.0824659e-01, -1.0311496e-01,
-6.2620710e-02],
[-1.0530860e-02, -1.4710520e-02, -1.2073830e-02, 1.4872750e-02,
2.9128660e-01, -3.0428475e-01, -4.7932048e-01, 5.4627526e-01,
3.3828667e-01],
[ 3.2361560e-02, -2.7793500e-01, -2.0555880e-02, 1.2225306e-01,
9.4021680e-02, 6.1644112e-01, -2.9559583e-01, -3.5519094e-01,
1.8827311e-01],
[ 6.7806630e-01, -3.1486974e-01, -5.8859340e-02, 4.6893594e-01,
-1.5144619e-01, -2.6948154e-01, -8.3005570e-02, -1.1724050e-01,
6.4839130e-02],
[ 1.2246510e-01, -2.2264460e-01, -1.1234940e-02, 1.7741348e-01,
-6.5091490e-02, 3.4337489e-01, 4.8461518e-01, 5.6309954e-01,
-3.0563622e-01],
[-9.8430410e-02, 1.0625540e-02, 3.2209196e-01, 3.7000000e-07,
6.0000000e-08, -1.6000000e-07, 4.7000000e-07, -0.0000000e+00,
8.0000000e-08],
[ 2.3008060e-02, -3.6648300e-03, 8.9105393e-01, 5.7000000e-07,
2.0000000e-08, 8.0000000e-08, 1.3300000e-06, -3.0000000e-08,
-4.0000000e-08],
[ 6.7576590e-02, 1.9393890e-02, 1.7925217e-01, 2.1000000e-07,
-1.9000000e-07, -7.0000000e-08, 2.6000000e-07, 9.0000000e-08,
-0.0000000e+00]])
wi = np.array([1.24800e+01, 1.21200e+01, 7.51000e+00, 0.00000e+00, 3.00000e-02,
4.00000e-02, 1.67567e+03, 1.67576e+03, 1.67577e+03, 1.90370e+03,
1.90373e+03, 3.52600e+03, 3.78663e+03, 3.78675e+03, 3.78678e+03])
wf = np.array([9.58000e+00, 5.22000e+00, 4.88000e+00, 4.70000e+00, 2.00000e-02,
1.30000e-01, 2.10000e-01, 1.90700e+01, 2.37100e+01, 8.65110e+02,
1.71559e+03, 1.71581e+03, 3.55823e+03, 3.82900e+03, 3.82936e+03])
Ud, delta = qchem.duschinsky(Li, Lf, ri, rf, wf, m)
plt.imshow(abs(Ud), cmap="Greens")
plt.colorbar()
plt.xlabel("Mode index")
plt.ylabel("Mode index")
plt.tight_layout()
plt.show()
```

Thanks again for any help