import requests

headers = {"Authorization": "Bearer ps_xxxxx"}
base = "https://api.paperstack.qzz.io"

# Full paper — questions in global order (1-200)
paper = requests.get(f"{base}/neet/2024/s1/paper.json", headers=headers).json()
print(f"Total questions: {paper['total']}")

# Subject extract — questions renumbered 1..N
physics = requests.get(f"{base}/neet/2024/s1/physics.json", headers=headers).json()
print(f"Physics questions: {physics['total']}")

# List years
years_resp = requests.get(f"{base}/neet").json()
print(f"Available years: {years_resp['years']}")

# List shifts for a year
shifts_resp = requests.get(f"{base}/neet/2024").json()
for shift in shifts_resp["shifts"]:
    print(f"{shift['shift']}: {shift['total']} questions, checksum: {shift['checksum']}")

import json

data = requests.get(f"{base}/neet/2024/s1/paper.json", headers=headers).json()

mcqs = [q for q in data["questions"] if q["type"] == "mcq"]
nat_questions = [q for q in data["questions"] if q["type"] == "nat"]
print(f"MCQs: {len(mcqs)}, NATs: {len(nat_questions)}")

from collections import Counter

topic_counts = Counter(q["topic"] for q in data["questions"])
for topic, count in topic_counts.most_common(10):
    print(f"{topic}: {count} questions")

# Find high-value questions
high_value = [q for q in data["questions"] if q["marks"] >= 4]
print(f"High-value questions: {len(high_value)}")

# Calculate total marks
total_marks = sum(q["marks"] for q in data["questions"])
print(f"Total possible marks: {total_marks}")

import random

def create_practice_set(questions, count=10):
    selected = random.sample(questions, min(count, len(questions)))
    return [
        {
            "id": q["id"],
            "question": q["text"],
            "type": q["type"],
            "options": q["options"],
            "topic": q["topic"],
            "marks": q["marks"],
        }
        for q in selected
    ]

practice = create_practice_set(data["questions"], 10)
print(json.dumps(practice, indent=2))

def get_questions_by_topic(questions, topic):
    return [q for q in questions if q["topic"] == topic]

# Practice specific topics
kinematics = get_questions_by_topic(data["questions"], "kinematics")
print(f"Found {len(kinematics)} kinematics questions")

import os

def download_diagrams(questions, headers, base, exam, year, shift):
    os.makedirs("diagrams", exist_ok=True)
    for q in questions:
        if q.get("diagrams"):
            for diagram in q["diagrams"]:
                file = diagram["file"]
                resp = requests.get(
                    f"{base}/{exam}/{year}/{shift}/{file}",
                    headers=headers
                )
                local_name = f"{q['id']}_{os.path.basename(file)}"
                with open(f"diagrams/{local_name}", "wb") as f:
                    f.write(resp.content)
                print(f"Downloaded: {local_name}")

import json
from collections import Counter
import matplotlib.pyplot as plt

topics = Counter(q["topic"] for q in data["questions"])

# Plot top 15 topics
top_15 = topics.most_common(15)
labels, values = zip(*top_15)

plt.figure(figsize=(10, 6))
plt.barh(range(len(labels)), values)
plt.yticks(range(len(labels)), labels)
plt.xlabel("Question count")
plt.title(f"Topic Distribution — NEET 2024 Physics")
plt.gca().invert_yaxis()
plt.tight_layout()
plt.savefig("topic_distribution.png")

from collections import Counter

difficulty = Counter(q.get("difficulty") for q in data["questions"])
for level, count in difficulty.most_common():
    print(f"{level or 'unrated'}: {count} questions ({count/len(data['questions'])*100:.0f}%)")

type_counts = Counter(q["type"] for q in data["questions"])
total = len(data["questions"])
for qtype, count in type_counts.most_common():
    print(f"{qtype}: {count} ({count/total*100:.0f}%)")

import hashlib

# Compute checksum locally
content = json.dumps(data, separators=(",", ":")).encode("utf-8")
local_hash = hashlib.sha256(content).hexdigest()

# Compare with API-reported checksum
assert local_hash == data["checksum"], "Data integrity check failed!"
print("Checksum verified ✓")

const headers = { Authorization: "Bearer ps_xxxxx" };
const base = "https://api.paperstack.qzz.io";

async function fetchPaper(exam, year, shift, file) {
  const res = await fetch(`${base}/${exam}/${year}/${shift}/${file}`, { headers });
  return res.json();
}

// Usage
const paper = await fetchPaper("neet", "2024", "s1", "paper.json");
console.log(`Schema: ${paper.schema}, Questions: ${paper.total}`);
console.log(`Checksum: ${paper.checksum}`);

// Group by subject
const bySubject = {};
for (const q of paper.questions) {
  (bySubject[q.subject] ??= []).push(q);
}
console.log(Object.keys(bySubject));

def validate_paper(data):
    required = ["schema", "exam", "year", "shift", "subjects", "total", "questions"]
    for field in required:
        assert field in data, f"Missing field: {field}"
    assert data["schema"] == "v4", f"Unexpected schema: {data['schema']}"
    assert len(data["questions"]) == data["total"], "Question count mismatch"
    print("Response valid ✓")

import time

def fetch_with_retry(url, headers, max_retries=3):
    for attempt in range(max_retries):
        resp = requests.get(url, headers=headers)
        if resp.status_code == 200:
            return resp.json()
        if resp.status_code == 429:
            wait = min(2 ** attempt, 30)
            print(f"Rate limited. Waiting {wait}s...")
            time.sleep(wait)
            continue
        resp.raise_for_status()
    raise Exception("Max retries exceeded")