Examples¶

# 01_MAIN.md

## Abstract
This is a simple example demonstrating basic Rxiv-Maker features.

## Introduction
Scientific writing doesn't have to be complicated [@smith2023].

## Methods
We analyzed data using Python scripts (Figure @fig:results).

![Analysis results](FIGURES/simple_plot.py)
{#fig:results}

## Conclusion
Rxiv-Maker simplifies manuscript preparation.

## References

![System architecture diagram](FIGURES/architecture.png)
{#fig:architecture width="0.8\\linewidth"}

As shown in Figure @fig:architecture, the system has three components.

![**Comprehensive Analysis.** **(A)** Time series showing signal evolution.
**(B)** Frequency distribution of signals. **(C)** Correlation matrix between
experimental variables. **(D)** Principal component analysis revealing distinct
clusters. All panels use data from the same experimental run (n=1000 measurements).](FIGURES/multi_panel_figure.py)
{#fig:comprehensive width="\\textwidth" tex_position="t"}

{{py:exec
import pandas as pd
import numpy as np
from scipy import stats

# Load experimental data
df = pd.read_csv("DATA/experiment.csv")

# Calculate statistics
control_mean = df[df['group']=='control']['value'].mean()
treatment_mean = df[df['group']=='treatment']['value'].mean()
t_stat, p_value = stats.ttest_ind(
    df[df['group']=='control']['value'],
    df[df['group']=='treatment']['value']
)
effect_size = (treatment_mean - control_mean) / df['value'].std()
}}

Our analysis of {{py:get len(df)}} samples revealed a significant difference
between control (M={{py:get control_mean:.2f}}) and treatment
(M={{py:get treatment_mean:.2f}}) groups (t={{py:get t_stat:.2f}},
p={{py:get p_value:.4f}}, Cohen's d={{py:get effect_size:.2f}}).

## Results

We analyzed {{py:get num_cells}} single cells using our pipeline
(Figure @fig:pipeline). The analysis revealed {{py:get num_clusters}}
distinct cell populations with expression patterns shown in Figure @fig:heatmap.

![Analysis pipeline](FIGURES/pipeline_diagram.py)
{#fig:pipeline}

![Expression heatmap](FIGURES/expression_heatmap.py)
{#fig:heatmap}

## Experimental Results

The resonance frequency was measured as {{py:get freq_resonance:.3f}} GHz
with Q-factor of {{py:get q_factor:.1f}} (Figure @fig:spectrum).

$$
Q = \frac{f_0}{\Delta f}
$$
{#eq:q_factor}

where $f_0$ is the resonance frequency and $\Delta f$ is the bandwidth.

## Model Performance

The trained model achieved {{py:get accuracy:.2%}} accuracy on the test set
(n={{py:get test_size}} samples). Confusion matrix and ROC curves are shown
in Figure @fig:performance.

![Model performance metrics](FIGURES/model_performance.py)
{#fig:performance width="\\textwidth"}