Datasets:
File size: 4,268 Bytes
479b033 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | 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 = [
# "o1-low",
# "o1-medium",
# "o1-high",
# "o3-low",
# "o3-medium",
# "o3-high",
# "o3-mini-low",
# "o3-mini-medium",
# "o3-mini-high",
# "o4-mini-low",
# "o4-mini-medium",
# "o4-mini-high",
# "o1-mini",
# "deepseek-reasoner",
# "gemini-2.0-flash",
# "gemini-1.5-flash",
# "gemini-2.5-pro-exp-03-25",
# "gemini-2.0-flash-lite-preview-02-05",
# "claude-3-7-sonnet-20250219"
]
# num_repeats = 1
# cache = asyncio.run(
# run_model_prompt_queries(
# models=models,
# prompts=message,
# titles=title,
# num_elems=num_elems,
# num_repeats=[[1]]
# )
# )
# cache = asyncio.run(
# run_model_prompt_queries(
# models=models,
# prompts=message,
# titles=titles,
# num_elems=num_elems,
# num_repeats=1
# )
# )
# reset_db()
# now we know it’s done:
# print(get_all_results("test", models))
# for model in models:
# get_results("ARPES Bandstructure", model, save_file="model.jsonl")
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