| from serve import run_model_prompt_queries |
| import random, numpy as np |
| from db import get_all_results, reset_db, save_result |
| import asyncio |
|
|
| def array2string(array): |
| return '\n'.join(['\t'.join(map(str, row)) for row in array]) |
|
|
| E_pnts = 51 |
| k_pnts = 101 |
|
|
| E_min_0 = 2; E_max_0 = 3 |
| k_min_0 = -1; k_max_0 = 1 |
| tau_0 = 3 |
| noise_0 = 0.3 |
|
|
| E_min_1 = 5; E_max_1 = 7 |
| k_min_1 = -1; k_max_1 = 1 |
| tau_1 = 1 |
| noise_1 = 0.2 |
|
|
| dE_0 = (E_max_0 - E_min_0)/(E_pnts - 1) |
| dk_0 = (k_max_0 - k_min_0)/(k_pnts - 1) |
| Ex_0 = 2.5 |
|
|
| dE_1 = (E_max_1 - E_min_1)/(E_pnts - 1) |
| dk_1 = (k_max_1 - k_min_1)/(k_pnts - 1) |
| Ex_1 = 5.0 |
|
|
| E_array_0 = np.linspace(E_min_0, E_max_0, E_pnts, endpoint = True) |
| k_array_0 = np.linspace(k_min_0, k_max_0, k_pnts, endpoint = True) |
|
|
| E_array_1 = np.linspace(E_min_1, E_max_1, E_pnts, endpoint = True) |
| k_array_1 = np.linspace(k_min_1, k_max_1, k_pnts, endpoint = True) |
|
|
| spectrum_array_0 = np.zeros((E_pnts, k_pnts)) |
| spectrum_array_1 = np.zeros((E_pnts, k_pnts)) |
|
|
| def Ek_0(k, Ex): |
| E = k + Ex |
| return E |
|
|
| def Ek_1(k, Ex): |
| E = 3*k + Ex |
| return E |
|
|
| def Ak_0(E, k, tau, Ex): |
| A = 1/(1 + ((E - Ek_0(k, Ex))**2)*((2*tau)**2)) |
| return A |
|
|
| def Ak_1(E, k, tau, Ex): |
| A = 1/(1 + ((E - Ek_1(k, Ex))**2)*((2*tau)**2)) |
| return A |
|
|
| k_dispersion_array_0 = np.zeros(E_pnts) |
| k_dispersion_array_1 = np.zeros(E_pnts) |
| k_trace_array_1 = np.zeros(E_pnts) |
|
|
| for num in range(E_pnts): |
| k_dispersion_array_0[num] = E_array_0[num] - Ex_0 |
| k_dispersion_array_1[num] = (E_array_1[num] - Ex_1)/3 |
|
|
| for E_num in range(E_pnts): |
| k_trace_array_1[E_num] = (E_array_1[E_num] - Ex_1)/3 |
| for k_num in range(k_pnts): |
| spectrum_array_0[E_num,k_num] = Ak_0(E_array_0[E_num], k_array_0[k_num], tau_0, Ex_0) + random.random()*noise_0 |
| spectrum_array_1[E_num,k_num] = Ak_1(E_array_1[E_num], k_array_1[k_num], tau_1, Ex_1) + random.random()*noise_1 |
|
|
| temp_0 = np.array(E_array_0)[:, np.newaxis] |
| temp_1 = np.array(E_array_1)[:, np.newaxis] |
|
|
| data_0 = array2string(np.concatenate((np.round(temp_0, decimals = 3), np.round(spectrum_array_0, decimals = 3)), axis = 1)) |
| data_1 = array2string(np.concatenate((np.round(temp_1, decimals = 3), np.round(spectrum_array_1, decimals = 3)), axis = 1)) |
|
|
| content = "Dataset A:\nEnergy (eV) \t Intensity \n" + "Spectrum:\t" + '\t'.join(map(str, k_array_1)) + "\n" + data_1 + "Dataset B:\nEnergy (eV) \t Intensity \n" + "Spectrum:\t" + '\t'.join(map(str, k_array_0)) + "\n" + data_0 |
|
|
| prompt = 'You are given two ARPES datasets in the form of tables.' \ |
| 'Their bandstructure is the set of momenta corresponding to the maximum spectral intensity at each energy. Here is an example. '\ |
| 'Read Dataset A. Its bandstructure is given by the array: [' + ','.join(str(x) for x in k_trace_array_1) + f']. '\ |
| 'Now read Dataset B. What are the momenta of the bandstructure for each energy? '\ |
| 'Do not print any files read, or code written. Return only an array of {E_pnts} numbers}, with no text.' |
|
|
| message = [f"{content}\n\n{prompt}"] |
| num_elems = [E_pnts] |
| message = [f"return [2 for _ in range(3)] as array of three numbers.]"] |
| num_elems = [3] |
|
|
| title = ["test"] |
|
|
| models = [ |
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