5  PDF to Markdown with Memory

from pyhere import here
import sys
import os
from pathlib import Path
from openai import OpenAI
from IPython.display import display_markdown

sys.path.append(os.path.abspath('../..'))

api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)

5.1 Final Wrapper

from src.openai_tools.ocr_md_batch import ocr_pdf_batch_to_markdown
from src.fs import write_text_file

%%time
tirads_md = ocr_pdf_batch_to_markdown(here("pdf/paper/TI-RADS_A User’s Guide.pdf"),
                                       model = "gpt-4o", batch_size = 3)

CPU times: user 522 ms, sys: 27.4 ms, total: 549 ms
Wall time: 2min 9s

write_text_file(tirads_md, here("output/markdown/TI-RADS_A-User-Guide_batched.md"))

Text successfully written to /Users/kittipos/Documents/LLM/llm-notes/output/markdown/TI-RADS_A-User-Guide_batched.md.

5.2 PDF -> Base64 Image

from src.pdftools import pdf_to_base64_images

cochran_img_base64 = pdf_to_base64_images(here("pdf/textbook/Cochran_1977_SamplingTechniques_Ch1.pdf"))
cochran_pg14_img_base64 = pdf_to_base64_images(here("pdf/textbook/Cochran_1977_SamplingTechniques_Ch1_pg14.pdf"))

cochran_img_base64[0:3]

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',
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',
 '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']

5.3 OCR batched of Images to List of Markdown

from src.openai_tools.mem_vision import get_completions_vision_mem_df

def ocr_batch_image_to_markdown(base64_images, 
                                model = "gpt-4o",
                                md_format = "Github-flavored markdown",
                                heading_lv_max = "H2"
                                ):
    system_prompt = f"""
    You are an advanced OCR-based data extraction tool designed to convert text, tables, and structured content from images of document pages into {md_format}. 
    - Each image will represent a single page of a document.
    - Ensure the output retains the original layout and information integrity as closely as possible. Include headers, bullet points, or tables where appropriate, and optimize for readability in Markdown syntax.
    
    Here are the Markdown specification:
    **Heading level:** The highest level of heading is {heading_lv_max}. 
    **LaTeX Math expression**
    - Inline: surround the inline expression with dollar symbols, for example: $1+1 = 2$
    - Blocks: delimit the block expression with two dollar symbols, for example:
      $$
      E = m \times c^2 
      $$
    
    Return markdown text output without enclosing in code block. If any page is blank or no appropriate content can be extracted, return empty text string ("").  
    """
    
    response_df = get_completions_vision_mem_df(image_prompt="Convert data from this page to markdown text",
                                                image_prompt_next="Next page",
                                                base64_images=base64_images,
                                                system_prompt=system_prompt,
                                                model=model)
    
    response_ls = response_df["assistant_text"].to_list()
    
    return response_ls

cochran_img_md_0_3 = ocr_batch_image_to_markdown(base64_images = cochran_img_base64[0:3])

display_markdown(print(cochran_img_md_0_3[0])) 
print("\n\n---\n\n")
display_markdown(print(cochran_img_md_0_3[1])) 
print("\n\n---\n\n")
display_markdown(print(cochran_img_md_0_3[2])) 

## Sampling Techniques

*third edition*

**WILLIAM G. COCHRAN**

*Professor of Statistics, Emeritus  
Harvard University*

**JOHN WILEY & SONS**  
New York • Chichester • Brisbane • Toronto • Singapore


---


Copyright © 1977, by John Wiley & Sons, Inc.

All rights reserved. Published simultaneously in Canada.

Reproduction or translation of any part of this work beyond that permitted by Sections 107 or 108 of the 1976 United States Copyright Act without the permission of the copyright owner is unlawful. Requests for permission or further information should be addressed to the Permissions Department, John Wiley & Sons, Inc.

**Library of Congress Cataloging in Publication Data:**  
Cochran, William Gemmell, 1909—
Sampling techniques.

(Wiley series in probability and mathematical statistics)  
Includes bibliographical references and index.  
1. Sampling (Statistics) I. Title.

QA276.6.C6 1977 001.4'222 77-728  
ISBN 0-471-16240-X

Printed in the United States of America

40 39 38 37 36


---


to Betty

5.4 Extract Multiple Images Batches

def ocr_image_batch_to_markdown(base64_images: list[str] | str, batch_size = 3, **kwarg):
    """Convert one or multiple base64-encoded images to Markdown text with batched memory."""
    
    # Single Page
    is_single_image = all([len(x) == 1 for x in base64_images])
    if is_single_image:
        md_text = ocr_batch_image_to_markdown(base64_images, **kwarg)
        return md_text
    
    # Multiple Pages
    base64_images_batched = _slice_list(base64_images, batch_size = batch_size)
    
    out_ls_nested = [] # Will be Nested list
    
    for base64_images_ls in base64_images_batched:
        md_text_ls = ocr_batch_image_to_markdown(base64_images_ls, **kwarg)
        md_text_ls_rm_blank = list(filter(None, md_text_ls)) # Remove blank string ("")
        out_ls_nested.append(md_text_ls_rm_blank) 
        
    out_ls = [item for sublist in out_ls_nested for item in sublist] # Un-nest List
    md_text = "\n\n---\n\n".join(out_ls)
        
    return md_text
    

cochran_img_md_0_9 = ocr_image_batch_to_markdown(cochran_img_base64[0:9])

cochran_img_md_0_9

"## Sampling Techniques\n\n*third edition*\n\n**WILLIAM G. COCHRAN**\n\n*Professor of Statistics, Emeritus  \nHarvard University*\n\n**JOHN WILEY & SONS**  \nNew York · Chichester · Brisbane · Toronto · Singapore\n\n---\n\nCopyright © 1977, by John Wiley & Sons, Inc.\n\nAll rights reserved. Published simultaneously in Canada.\n\nReproduction or translation of any part of this work beyond that permitted by Sections 107 or 108 of the 1976 United States Copyright Act without the permission of the copyright owner is unlawful. Requests for permission or further information should be addressed to the Permissions Department, John Wiley & Sons, Inc.\n\n**Library of Congress Cataloging in Publication Data:**\n\nCochran, William Gemmell, 1909-  \nSampling techniques.\n\n(Wiley series in probability and mathematical statistics)  \nIncludes bibliographical references and index.\n\n1. Sampling (Statistics) I. Title.  \nQA276.6.C6 1977 001.4'222 77-728  \nISBN 0-471-16240-X\n\nPrinted in the United States of America\n\n40 39 38 37 36\n\n---\n\nto Betty\n\n---\n\n## Preface\n\nAs did the previous editions, this textbook presents a comprehensive account of sampling theory as it has been developed for use in sample surveys. It contains illustrations to show how the theory is applied in practice, and exercises to be worked by the student. The book will be useful both as a text for a course on sample surveys in which the major emphasis is on theory and for individual reading by the student.\n\nThe minimum mathematical equipment necessary to follow the great bulk of the material is a familiarity with algebra, especially relatively complicated algebraic expressions, plus a knowledge of probability for finite sample spaces, including combinatorial probabilities. The book presupposes an introductory statistics course that covers means and standard deviations, the normal, binomial, hypergeometric, and multinomial distributions, the central limit theorem, linear regression, and the simpler types of analyses of variance. Since much of classical sample survey theory deals with the distributions of estimators over the set of randomizations provided by the sampling plan, some knowledge of nonparametric methods is helpful.\n\nThe topics in this edition are presented in essentially the same order as in earlier editions. New sections have been included, or sections rewritten, primarily for one of three reasons: (1) to present introductions to topics (sampling plans or methods of estimation) relatively new in the field; (2) to cover further work done during the last 15 years on older methods, intended either to improve them or to learn more about the performance of rival methods; and (3) to shorten, clarify, or simplify proofs given in previous editions.\n\nNew topics in this edition include the approximate methods developed for the difficult problem of attaching standard errors or confidence limits to nonlinear estimates made from the results of surveys with complex plans. These methods will be more and more needed as statistical analyses (e.g., regressions) are performed on the results. For surveys containing sensitive questions that some respondents are unlikely to be willing to answer truthfully, a new device is to present the respondent with either the sensitive question or an innocuous question; the specific choice, made by randomization, is unknown to the interviewer. In some sampling problems it may seem economically attractive, or essential in countries with limited sampling resources, to use two overlapping lists (or frames, as they are called) to cover the complete population. The method of double sampling has been extended to cases where the objective is to compare the means.\n\n---\n\nThe number of subgroups within the population. There has been interesting work on the attractive properties that the ratio and regression estimators have if it can be assumed that the finite population is itself a random sample from an infinite superpopulation in which a mathematical model appropriate to the ratio or regression estimator holds. This kind of assumption is not new—I noticed recently that Laplace used it around 1800 in a sampling problem—but it clarifies the relation between sample survey theory and standard statistical theory.\n\nAn example of further work on topics included in previous editions is Chapter 9a, which has been written partly from material previously in Chapter 9; this was done mainly to give a more adequate account of what seem to me the principal methods produced for sampling with unequal probabilities without replacement. These include the similar methods given independently by Brewer, J. N. K. Rao, and Durbin, Murthy’s method, the Rao, Hartley, Cochran method, and Madow’s method related to systematic sampling, with comparisons of the performances of the methods on natural populations. New studies have been done on the sizes of components of errors of measurement in surveys by repeat measurements by different interviewers, by interpenetrating subsamples, and by a combination of the two approaches. For the ratio estimator, data from natural populations have been used to appraise the small-sample biases in the standard large-sample formulas for the variance and the estimated variance. Attempts have also been made to use lectures based variants of the ratio estimator itself and of the formula for estimating its sampling variance. In stratified sampling there has been additional work on allocating sample sizes to strata when more than one item is of importance and on estimating sample errors when only one unit is to be selected per stratum. Some new systematic sampling methods for handling populations having linear trends are also of interest.\n\nAlva I. Funkner and Emil H. Jebe prepared a large part of the lecture notes from which the first edition of this book was written. Some investigations that provided background material were supported by the Office of Naval Research, Navy Department. From discussions of recent developments in sampling or suggestions about this edition, I have been greatly helped by Tore Dalenius, David J. Finney, Daniel G. Horvitz, Leslie Kish, P. S. R. S. Ambasiva Rao, Martin Sandelands, Joseph Sedransk, Arnold R. Sen, and especially J. N. K. Rao, whose painstaking reading of the new and revised sections of this edition resulted in many constructive suggestions about gaps, weaknesses, obscurities, and selection of topics. For typing and other work involved in production of a typescript I am indebted to Rowena Foss, Holly Grano, and Edith Klotz. My thanks to all.\n\nWilliam G. Cochran  \nSouth Orleans, Massachusetts  \nFebruary, 1977\n\n---\n\n## Contents\n\n| CHAPTER | PAGE |\n|---------|------|\n| 1 INTRODUCTION | 1 |\n\n### 1.1 Advantages of the Sampling Method\n- Page 1\n\n### 1.2 Some Uses of Sample Surveys\n- Page 2\n\n### 1.3 The Principal Steps in a Sample Survey\n- Page 4\n\n### 1.4 The Role of Sampling Theory\n- Page 8\n\n### 1.5 Probability Sampling\n- Page 9\n\n### 1.6 Alternatives to Probability Sampling\n- Page 11\n\n### 1.7 Use of the Normal Distribution\n- Page 12\n\n### 1.8 Bias and Its Effects\n- Page 15\n\n### 1.9 The Mean Square Error\n- Page 15\n\n**Exercises**\n- Page 16\n\n---\n\n| CHAPTER | PAGE |\n|---------|------|\n| 2 SIMPLE RANDOM SAMPLING | 18 |\n\n### 2.1 Simple Random Sampling\n- Page 18\n\n### 2.2 Selection of a Simple Random Sample\n- Page 20\n\n### 2.3 Definitions and Notation\n- Page 23\n\n### 2.4 Properties of the Estimates\n- Page 25\n\n### 2.5 Variances of the Estimates\n- Page 26\n\n### 2.6 The Finite Population Correction\n- Page 32\n\n### 2.7 Estimation of the Standard Error from a Sample\n- Page 34\n\n### 2.8 Confidence Limits\n- Page 35\n\n### 2.9 An Alternative Method of Proof\n- Page 37\n\n### 2.10 Random Sampling with Replacement\n- Page 38\n\n### 2.11 Estimation of a Ratio\n- Page 39\n\n### 2.12 Estimates of Means Over Subpopulations\n- Page 41\n\n### 2.13 Estimates of Totals Over Subpopulations\n- Page 42\n\n### 2.14 Comparisons Between Domain Means\n- Page 43\n\n### 2.15 Validity of the Normal Approximation\n- Page 44\n\n### 2.16 Linear Estimators of the Population Mean\n- Page 45\n\n**Exercises**\n- Page 45\n\n---\n\n## Contents\n\n| CHAPTER | PAGE |\n|---------|------|\n| 3 SAMPLING PROPORTIONS AND PERCENTAGES | 50 |\n\n### 3.1 Qualitative Characteristics\n- Page 50\n\n### 3.2 Variances of the Sample Estimates\n- Page 52\n\n### 3.3 The Effect of $p$ on the Standard Errors\n- Page 53\n\n### 3.4 The Binomial Distribution\n- Page 54\n\n### 3.5 The Hypergeometric Distribution\n- Page 56\n\n### 3.6 Confidence Limits\n- Page 57\n\n### 3.7 Classification into More than Two Classes\n- Page 59\n\n### 3.8 Confidence Limits with More than Two Classes\n- Page 61\n\n### 3.9 The Conditional Distribution of $p$\n- Page 62\n\n### 3.10 Proportions and Totals Over Subpopulations\n- Page 63\n\n### 3.11 Comparisons Between Different Domains\n- Page 64\n\n### 3.12 Estimation of Proportions in Cluster Sampling\n- Page 65\n\n**Exercises**\n- Page 68\n\n---\n\n| CHAPTER | PAGE |\n|---------|------|\n| 4 THE ESTIMATION OF SAMPLE SIZE | 72 |\n\n### 4.1 A Hypothetical Example\n- Page 72\n\n### 4.2 Analysis of the Problem\n- Page 73\n\n### 4.3 The Specification of Precision\n- Page 74\n\n### 4.4 The Formula for $n$ in Sampling for Proportions\n- Page 75\n\n### 4.5 Rare Items—Inverse Sampling\n- Page 76\n\n### 4.6 The Formula for $n$ with Continuous Data\n- Page 77\n\n### 4.7 Advance Estimates of Population Variances\n- Page 78\n\n### 4.8 Sample Size with More than One Item\n- Page 80\n\n### 4.9 Sample Size when Estimates Are Wanted for Subdivisions of the Population\n- Page 82\n\n### 4.10 Sample Size in Decision Problems\n- Page 85\n\n### 4.11 The Design Effect (Deff)\n- Page 86\n\n**Exercises**\n- Page 86\n\n---\n\n| CHAPTER | PAGE |\n|---------|------|\n| 5 STRATIFIED RANDOM SAMPLING | 89 |\n\n### 5.1 Description\n- Page 89\n\n### 5.2 Notation\n- Page 90\n\n### 5.3 Properties of the Estimates\n- Page 91\n\n### 5.4 The Estimated Variance and Confidence Limits\n- Page 93\n\n### 5.5 Optimum Allocation\n- Page 96\n\n---\n\n## Contents\n\n### 5.6 Relative Precision of Stratified Random and Simple Random Sampling\n- Page 99\n\n### 5.7 When Does Stratification Produce Large Gains in Precision?\n- Page 101\n\n### 5.8 Allocation Requiring More than 100 Per Cent Sampling\n- Page 104\n\n### 5.9 Estimation of Sample Size with Continuous Data\n- Page 105\n\n### 5.10 Stratified Sampling for Proportions\n- Page 107\n\n### 5.11 Gains in Precision in Stratified Sampling for Proportions\n- Page 109\n\n### 5.12 Estimation of Sample Size with Proportions\n- Page 110\n\n**Exercises**\n- Page 111\n\n---\n\n| CHAPTER | PAGE |\n|---------|------|\n| 5A FURTHER ASPECTS OF STRATIFIED SAMPLING | 115 |\n\n### 5A.1 Effects of Deviations from the Optimum Allocation\n- Page 115\n\n### 5A.2 Effects of Errors in the Stratum Sizes\n- Page 117\n\n### 5A.3 The Problem of Allocation with More than One Item\n- Page 121\n\n### 5A.4 Other Methods of Allocation with More than One Item\n- Page 123\n\n### 5A.5 Two-Way Stratification with Small Samples\n- Page 124\n\n### 5A.6 Controlled Selection\n- Page 126\n\n### 5A.7 The Construction of Strata\n- Page 128\n\n### 5A.8 Number of Strata\n- Page 136\n\n### 5A.9 Stratification After Selection of the Sample (Poststratification)\n- Page 137\n\n### 5A.10 Quota Sampling\n- Page 138\n\n### 5A.11 Estimation from a Sample of the Gain Due to Stratification\n- Page 142\n\n### 5A.12 Estimation of Variance with One Unit per Stratum\n- Page 143\n\n### 5A.13 Strata as Domain of Study\n- Page 144\n\n### 5A.14 Estimating Totals and Means Over Subpopulations\n- Page 145\n\n### 5A.15 Sampling from Two Frames\n- Page 146\n\n**Exercises**\n- Page 146\n\n---\n\n| CHAPTER | PAGE |\n|---------|------|\n| 6 RATIO ESTIMATORS | 150 |\n\n### 6.1 Methods of Estimation\n- Page 150\n\n### 6.2 The Ratio Estimate\n- Page 150\n\n### 6.3 Approximate Variance of the Ratio Estimate\n- Page 154\n\n### 6.4 Estimation of the Variance from a Sample\n- Page 155\n\n### 6.5 Confidence Limits\n- Page 157\n\n### 6.6 Comparison of the Ratio Estimate with Mean per Unit\n- Page 158\n\n### 6.7 Conditions Under Which the Ratio Estimate Is a Best Linear Unbiased Estimate\n- Page 161\n\n### 6.8 Bias of the Ratio Estimate\n- Page 162"

from pathlib import Path

file_path = Path(here("output/markdown/Cochran_1977_SamplingTechniques_Ch1_Batch_0-9.md"))

with open(file_path, 'w', encoding='utf-8') as file:
    # Write the string of text to the file
    file.write(cochran_img_md_0_9)

5.4.0.1 HowTo: Slice List to Sublist with Batch Size

def _slice_list(ls, batch_size):
    """Split a list into sublists with a maximum length of `batch_size`."""
    return [ls[i:(i + batch_size)] for i in range(0, len(ls), batch_size)]

# Example usage:
print(_slice_list(["a", "b", "c", "d", "e", "f", "g"], 2))
print(_slice_list(["a", "b", "c", "d", "e", "f", "g"], 3))

[['a', 'b'], ['c', 'd'], ['e', 'f'], ['g']]
[['a', 'b', 'c'], ['d', 'e', 'f'], ['g']]