Myra Sizer
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Physiology, Glucocorticoids StatPearls NCBI Bookshelf
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---
## Overview
This guide explains how to create a **single‑file PDF** that contains all of your web pages (the original HTML files, their CSS stylesheets, JavaScript assets, images, fonts, etc.). The result is a stand‑alone document that can be opened on any computer without requiring an Internet connection or a web server.
---
## Step 1 – Gather All Files
1. **Copy the entire project folder** (the root of your website).
2. Verify that the folder contains:
- `index.html` and other HTML pages.
- Sub‑folders: `css/`, `js/`, `images/`, `fonts/`, etc.
3. If you use a build tool (Webpack, Gulp, etc.), run its *build* command so that all assets are in their final form.
---
## Step 2 – Create an HTML "Index" Page
This page will serve as the starting point for printing:
```html
Print Demo
Demo Page
This is a test of printing.
```
**Tip:** Keep the CSS minimal or inline to avoid external dependencies.
---
## 3. Printing and Scanning
### 3.1 How to Print
- **Print**: `Ctrl + P` (Windows) / `Cmd + P` (Mac).
- Choose a printer, set pages, and click "Print".
### 3.2 How to Scan
- **Scan**: Use the scanner’s software or Windows’ "Scanner" app.
- Save scanned images as PNG/JPG.
---
## 4. Converting Images to Text
### 4.1 OCR (Optical Character Recognition)
Use an OCR tool like Google Cloud Vision, Tesseract, or Adobe Acrobat:
```bash
tesseract image.png output_text -l eng
```
- `image.png`: scanned PNG file
- `output_text.txt`: extracted text
---
## 5. Working with the Data
### 5.1 Extracting a Dictionary from JSON
```python
import json
with open('data.json', 'r') as f:
data = json.load(f)
# Assuming keys are strings like "0", "1", etc.
dict_data = int(k): v for k, v in data.items()
```
### 5.2 Converting JSON to YAML
```python
import yaml
with open('data.json', 'r') as f:
json_dict = json.load(f)
yaml_string = yaml.dump(json_dict, sort_keys=False)
print(yaml_string)
```
### 5.3 Pretty Printing a Dictionary
```python
from pprint import pprint
pprint(dict_data, width=1) # Each key-value pair on its own line
```
---
## 4. Common Pitfalls & Tips
| Scenario | What to Avoid | Recommendation |
|----------|---------------|----------------|
| **Large JSON files** | Loading entire file into memory (`json.load`) | Use `ijson` for streaming or read in chunks if the structure allows |
| **Indentation errors** when writing YAML | Mixing tabs and spaces | Configure your editor to use spaces only (usually 2–4 per level) |
| **Nested dicts with many levels** | Manual pretty‑printing becomes hard | Use `pprint` or a dedicated library (`yamale`, `ruamel.yaml`) that handles indentation automatically |
| **Non‑serializable objects** (e.g., datetime) | Directly dumping to JSON/YAML raises TypeError | Convert them to strings first (`isoformat()`) or write custom serializer functions |
| **Large files** | Memory usage spikes when loading entire file | Process line by line with streaming parsers (e.g., `jsonlines`, `ruamel.yaml` with `Loader=SafeLoader`) |
---
## 4. Summary of Key Take‑aways
| What to Do | Why It Matters |
|------------|----------------|
| **Convert data types before serialization** | JSON/YAML expect primitive types; complex objects break the process. |
| **Use safe loaders/dumpers** | Avoid code injection or accidental execution (e.g., `yaml.safe_load`). |
| **Handle large files with streaming** | Prevents memory exhaustion and improves performance. |
| **Validate schema/structure** | Ensures downstream systems can reliably consume data. |
| **Keep serialization consistent across environments** | Avoid platform‑specific quirks that could corrupt the file. |
By keeping these principles in mind, developers can smoothly transition data from Python objects into robust JSON or YAML files suitable for configuration, logging, or inter‑service communication.
---
## 4. Final Checklist
- Convert your Python object to a serializable structure (list/dict).
- Use `json.dump` or `yaml.safe_dump` with appropriate options.
- Verify the file is valid and readable by your target consumers.
- Handle errors (e.g., I/O, encoding) gracefully.
Happy coding!
