<jupyter_start><jupyter_text>The following code snippet shows, how you can import a transformer design, which is stored in a json and perform a calculation to estimate the main paramters. The transformer was originally published as a validation example in https://doi.org/10.3390/app10041361<jupyter_code>from importlib_resources import files
from src.two_winding_model import TransformerDesign, TwoWindingModel

path = files("data").joinpath("10MVA_example.json")

import json

with open(path) as json_file:
    data = json.load(json_file)

transformer = TransformerDesign.from_dict(data)

trafo_model = TwoWindingModel(input=transformer)
trafo_model.calculate(is_sc=False)

# FEM calculation
trafo_model.fem_simulation()

print(trafo_model.results)
print(trafo_model.hv_winding)
print(trafo_model.lv_winding)<jupyter_output>WindingDesign(inner_radius=230.0, thickness=35.3, winding_height=1100.0, filling_factor=70.0, current_density=2.65, mass=1129.3, dc_loss=19.2, ac_loss=1.4432870031532228, outer_radius=265.3, cable_length=0.0, amper_turns=72029.6)
{'fraction_non_alphanumeric': 0.09194312796208531, 'fraction_numerical': 0.06729857819905213, 'mean_word_length': 7.7272727272727275, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '108497', 'license': 'Apache-2.0', 'n_tokens_mistral': 400, 'n_tokens_neox': 360, 'n_words': 77, 'path': 'notes/6300kVA_transformer.ipynb', 'repo_name': 'tamasorosz/TrafoCalc', 'size': '1055'}