The Academic & Religious Impact of Wave Function of the Universe

Integration
Academic
Intro
Author

Kian Ghodoussi

Published

March 24, 2025

In the following notebook, we will be recreating Sturdy Statistics’ DeepDive page using the sturdy-stats-sdk.

In this notebook we will reproducing this deep dive analysis on every publication that cites the paper Wave function of the Universe authored by JB Hartle and Stephen Hawking in 1960. This paper spawned a series of downstream research fields and also had a sizeable impact on the relationship between religion and science, which we will begin to explore in this walkthrough.

Prerequisites

pip install sturdy-stats-sdk pandas numpy plotly

Code 
from IPython.display import display, Markdown, Latex
import pandas as pd
import numpy as np
import plotly.express as px
from sturdystats import Index, Job

from pprint import pprint
Code 
## Basic Utilities
px.defaults.template = "simple_white"  # Change the template
px.defaults.color_discrete_sequence = px.colors.qualitative.Dark24 # Change color sequence

def procFig(fig, **kwargs):
    fig.update_layout(plot_bgcolor= "rgba(0, 0, 0, 0)", paper_bgcolor= "rgba(0, 0, 0, 0)",
        margin=dict(l=0,r=0,b=0,t=30,pad=0),
        title_x=.5,
        **kwargs
    )
    fig.layout.xaxis.fixedrange = True
    fig.layout.yaxis.fixedrange = True
    return fig

def displayText(df, highlight):
    def processText(row):
        t = "\n".join([ f'1. {r["short_title"]}: {int(r["prevalence"]*100)}%' for r in row["paragraph_topics"][:5] ])
        x = row["text"]
        res = []
        for word in x.split(" "):
            for term in highlight:
                if term in word.lower() and "**" not in word:
                    word = "**"+word+"**"
            res.append(word)
        return f"<em>\n\n#### Result {row.name+1}/{df.index.max()+1}\n\n#### {row['published']}\n\n"+ t +"\n\n" + " ".join(res) + "</em>"

    res = df.apply(processText, axis=1).tolist()       
    display(Markdown(f"\n\n...\n\n".join(res)))

[Optional] Train Your Own Model

Sturdy Statistics integrates directly with Hacker News. Below we query the hackernews_comments integration for all comments that mention duckdb.

Training a model on our hacker news integration takes anywhere from 5-10 minutes. This step is optional and you can instead proceed with our public duckdb analysis index.

index = Index(id="index_d93b763e743a480a9f7e5f8f57c8c1b1")

# Uncomment the line below to create and train your own index
# index = Index(name="CN_Wave_Function_of_the_Universe") 

if index.get_status()["state"] == "untrained":
    index.ingestIntegration("cn_all", "https://doi.org/10.1103/PhysRevD.28.2960")
    index.train(dict(burn_in=1200, subdoc_hierarchy=False), fast=True, wait=False)
    print(job.get_status())
    # job.wait() # Sleeps until job finishes
Found an existing index with id="index_d93b763e743a480a9f7e5f8f57c8c1b1".

Exploration

In this section, we will demonstrate how to produce the two core visualization in their simplest form: the sunburst and the time trend plot.

index = Index(id="index_d93b763e743a480a9f7e5f8f57c8c1b1")
Found an existing index with id="index_d93b763e743a480a9f7e5f8f57c8c1b1".

Sunburst

Our bayesian probabilistic model learns a set of high level topics from your corpus. These topics are completely custom to your data, whether your dataset has hundreds of documents or billions. The model then maps this set of learned topics to single every word, sentence, paragraph, document, and group of documents to your dataset, providing a powerful semantic indexing.

This indexing enables us to store data in a granular, structured tabular format. This structured format enables rapid analysis to complex questions.

Topic Query

df = index.topicSearch()
df.head(5)[["short_title", "topic_group_short_title", "topic_id", "mentions", "prevalence"]]
short_title topic_group_short_title topic_id mentions prevalence
0 Quantum Gravity Theories Quantum Gravity Concepts 19 356.0 0.060771
1 Quantum Constraints and Invariance Theoretical Frameworks 16 250.0 0.044115
2 Cosmological Perturbations Cosmological Observations and Phenomena 35 243.0 0.040926
3 Anthropic Inflationary Landscape Cosmology 58 190.0 0.033849
4 Scalar Field Cosmology Cosmology 87 230.0 0.031586

Visualization

We can see there are two names: short_title and topic_group_short_title. The topic group is a high level thematic category while a topic is a much more granlular annotation.

A dataset can have hundreds of topics, but ussually only 20-50 topic groups. This hierarchy is extremly useful for organizing and exploring data in hierarchical formats such as sunbursts.

The inner circle of the sunburst is the title of the plot. The middle layer is the topic groups. And the leaf nodes are the topics that belong to the corresponding topic group. The size of each node is porportional to how often it shows up in the dataset.

df["title"] = "Wave Function of <br> the Universse Citations"
fig = px.sunburst(
    df,
    path=["title", "topic_group_short_title", "short_title"], 
    values="prevalence", hover_data={"topic_id": True}
)
procFig(fig, height=500).show()

Semantically Structured SQL

Sturdy Statistics embeds all of its semantic information into a tabular format. It directly exposes this tabular format through the queryMeta api.

In fact, all of our topic apis directly query the same tabular data structures that we expose in the queryMeta api.

Even More Granular Extraction

Every high level visualization or rollup can be instantly tied back to the original data, no matter how granular or complex.

Let’s saw we want to pull out all hacker news comments that discuss pandas and mention the topic Complex SQL Queries that happened in 2025. That is a simple API call

docdf = index.query(SEARCH_QUERY, topic_id=topic_id, semantic_search_cutoff=.5, limit=100, filters="published > '2015-01-01'")
print("Search:", SEARCH_QUERY, "Topic:", row.short_title.iloc[0])
displayText(docdf.iloc[[0, -1]], highlight=["duckdb", SEARCH_QUERY, "faster", "complex", "sql", "aggregate", "relational", "api"])
Search: religion Topic: Quantum Gravity Theories

Result 1/3

2015-03-25

  1. Theism and Modern Physics: 83%
  2. Quantum Gravity Theories: 11%
  3. Quantum Constraints and Invariance: 2%

Support has been lent to contemporary ‘New Atheism’ from physicalist interpretations of ‘hard’ science. From this perspective, any system of knowledge that does not rely solely upon empirical method is deemed meaningless in comparison to observationally-grounded empirical science. Consequently, as a non-empirically-based approach, faith positions are included in the critique offered by physicalists. The impetus for this article, then, is to establish physicalism as a reductionist epistemology that is partially comprised of – seemingly inconspicuously embedded – metaphysical assumptions. With metaphysics apparent in ‘hard’ science, it is argued from a Thomist perspective that metaphysical themes of primary causality must be realistically considered to account for being. As a logical outcome, the proposal is made that metaphysical primary causality directs to the reasonable suggestion that God creates. Intradisciplinary and/or interdisciplinary implications: This article specifically challenges the currently trendy ‘New Atheist’ school of thought, resting upon the counter-argument offered that ‘hard’ science cannot ultimately account for the emergence or continued existence of being. Utilising Aquinas, the research calls for a re-embracement of unified, as opposed to limited, systems of knowledge.

…

Result 3/3

2025-03-03

  1. Theism and Modern Physics: 58%
  2. Quantum Gravity Theories: 29%
  3. Emergence in Gravity: 3%
  4. Quantum Constraints and Invariance: 3%

The paper examines the main components of the Christian view of creation in light of a scientific worldview. Although no scientific theory can refute or corroborate the doctrine of creation, the current state of natural sciences does have some impact on the formation of creationism. The author underlines this influence and depicts the importance of a rational and aspectual approach to the Christian idea of creation and the Creator–creation relationship. The concept of creation is also presented as having significant consequences for the expression of various elements of religious doctrine, including the place of man in the world.

NB: our sql again corresponds to our search docs

assert df.loc[df["year"] >= 2015].publications.sum() == len(docdf)

Unlock Your Unstructured Data Today

from sturdystats import Index

index = Index("Custom Analysis")
index.upload(df.to_dict("records"))
index.commit()
index.train()

# Ready to Explore 
index.topicSearch()

More Examples