garland3

file = "blablabab.pdf"
import PyPDF2

all_comments = ""
# %%
# Open the PDF file
with open(file, 'rb') as pdf_file:
    # Create a PDF reader object
    pdf_reader = PyPDF2.PdfReader(pdf_file)

garland3

# %%
base = r"\Downloads\NL_Surrogate_Data\NL_Surrogate_Data"
import random
import matplotlib.pyplot as plt

from collections import OrderedDict
import os
import numpy as np
from typing import List
from dataclasses import dataclass

garland3

def check_duplicate_columns(df):
    # Identify duplicate columns
    duplicate_columns = df.columns[df.columns.duplicated(keep=False)]
    if len(duplicate_columns) == 0:
        print("No duplicate columns found.")
        return []
    else:
        print(f"Duplicates found. {duplicate_columns.to_list()} ")

    col_idxs_to_drop =[]

garland3

import numpy as np

class Accum_List(list):
    cnt_in_epoch = 0
    accum_fn = np.mean

    def __init__(self):
        super().__init__()
        self.internal_epoch_list = []

garland3

using Gridap
using Gridap.Geometry
const E = 70.0e9
const ν = 0.33
const λ = (E * ν) / ((1 + ν) * (1 - 2 * ν))
const μ = E / (2 * (1 + ν))
const density = 2710 # kg/m^3
σ(ε) = λ * tr(ε) * one(ε) + 2 * μ * ε

# for making the FEA grid. 

garland3

from pathlib import Path
from fpdf import FPDF
from PIL import Image

# Define directory to search for images
img_dir = Path("viz")

class MyPDF(FPDF):
    pass

garland3

#!/usr/bin/env python

import json,fire,re
from pathlib import Path

output_folder = 'rom'
output_folder_jl = output_folder+"_jl"
package_version = "0.1.0"

def is_export(cell):

garland3

"""
Script provides functional interface for Mish activation function.
"""

# import pytorch
import torch
import torch.nn.functional as F


# @torch.jit.script

garland3

import torch.nn as nn
class MLP(nn.Module):
    def __init__(self, input_dims = 1, out_dims = 1, hidden_size = 100, layers = 5):
        super(MLP, self).__init__()
        self.hidden_layer_size = hidden_size
        self.hidden_layers = layers
        
        layers = []
        input_size = input_dims
        for i in range(self.hidden_layers):

garland3

class Tracker:
    def __init__(self, names_of_things_to_track):
        self.names_of_things_to_track = names_of_things_to_track
        self.data = []
        
    def __call__(self, *values):
        self.add(*values)
        
    def add(self, *values):
        values = [try_detach(v) for v in values]