post_blog.py

import re
import os
import requests
import base64
import dotenv
import mistune

dotenv.load_dotenv()
api_endpoint = os.getenv('API_ENDPOINT')
password = os.getenv('BLOG_PASSWORD')
username = os.getenv('BLOG_USERNAME')

def generate_post_html(title, summary, mindmap, citation):
    html_summary = mistune.html(summary)
    post = f"""
<!-- wp:html -->
<div>
    <script src="https://cdn.jsdelivr.net/npm/markmap-autoloader@latest"></script>
    <style>
        .markmap {{
            position: relative;
        }}
        .markmap > svg {{
            width: 100%;
            border: 2px solid #000;
            height: 80dvh;
        }}
    </style>
    <p id="paper_summary" data="{summary.replace("&amp;", "&")}">
        {html_summary.replace("&amp;", "&")}
    </p>
    <br>
    <br>
    <h2>Mindmap</h2>
    <div class="markmap" data="{mindmap.replace("&amp;", "&")}">
        <script type="text/template">
            # {title.replace("&amp;", "&")}
            {mindmap.replace("&amp;", "&")}
        </script>
    </div>
    <br>
    <br>
    <h2>Citation</h2>
    <p id="paper_citation" data="{citation.replace("&amp;", "&")}">
        {mistune.html(citation.replace("&amp;", "&"))}
    </p>
</div>
<!-- /wp:html -->
    """
    return post

def sanitize_citation(citation):
    pattern = r"(https://doi\.org/\S+)"
    sanitized_citation = re.sub(
        pattern,
        lambda match: f"[{match.group(1)}](https://doi.org/{match.group(1).split('/')[-1]})",
        citation
    )
    return sanitized_citation


def create_post(title, category, summary, mindmap, citation):
    post_title = f"{title}"
    post_category = f"{category}"
    post_body = generate_post_html(title, summary, mindmap, sanitize_citation(citation))
    return post_title, post_category, post_body

def create_category_if_not_exists(category_name, headers):
    categories_url = f"{api_endpoint}/categories"
    response = requests.get(categories_url, headers=headers)
    if response.status_code == 200:
        categories = response.json()
        for category in categories:
            if category['name'].lower() == category_name.lower():
                return category['id']
    create_response = requests.post(
        categories_url,
        headers=headers,
        json={"name": category_name}
    )
    if create_response.status_code == 201:
        return create_response.json()['id']
    else:
        print(f"Error creating category: {create_response.text}")
        return None

def post_post(title, category, body):
    credentials = f"{username}:{password}"
    url = f"{api_endpoint}/posts"
    auth_key = base64.b64encode(credentials.encode('utf-8')).decode('utf-8')
    headers = {
        "Authorization": f"Basic {auth_key}"
    }
    category_ids = []
    category_id = create_category_if_not_exists(category, headers)
    if category_id:
        category_ids.append(category_id)
    post_data = {
        "title": title,
        "status": "publish",
        "categories": category_ids,
        "content": body
    }
    response = requests.post(url, headers=headers, json=post_data)
    print(response.status_code)
    if response.status_code == 201:
        return True
    else:
        return False
    
def main(title, category, summary, mindmap, citation, access_key):
    if access_key != os.getenv('ACCESS_KEY'):
        return False
    try:
        post_title, post_category, post_body = create_post(title, category, summary, mindmap, citation)
        status = post_post(post_title, post_category, post_body)
        if status:
            print('Post created successfully')
            return True
        else:
            print('Failed to create post')
            return False
    except Exception as e:
        print('An error occurred:', str(e))
        return False
    
if __name__ == '__main__':
    data = {
            "status": "success",
            "Astrophysics": {
                "2412.16344": {
                    "id": "2412.16344",
                    "doi": "https://doi.org/10.48550/arXiv.2412.16344",
                    "title": "Focal Plane of the Arcus Probe X-Ray Spectrograph",
                    "category": "Astrophysics",
                    "citation": "Grant, C. E., Bautz, M. W., Miller, E. D., Foster, R. F., LaMarr, B., Malonis, A., Prigozhin, G., Schneider, B., Leitz, C., &amp; Falcone, A. D. (2024). Focal Plane of the Arcus Probe X-Ray Spectrograph. ArXiv. https://doi.org/10.48550/ARXIV.2412.16344",
                    "summary": "## Summary\nThe Arcus Probe mission concept provides high-resolution soft X-ray and UV spectroscopy to study the universe. The X-ray Spectrograph (XRS) uses two CCD focal planes to detect and record X-ray photons. Laboratory performance results meet observatory requirements.\n\n## Highlights\n- The Arcus Probe mission concept explores the formation and evolution of clusters, galaxies, and stars.\n- The XRS instrument includes four parallel optical channels and two detector focal plane arrays.\n- The CCDs are designed and manufactured by MIT Lincoln Laboratory (MIT/LL).\n- The XRS focal plane utilizes high heritage MIT/LL CCDs with proven technologies.\n- Laboratory testing confirms CCID-94 performance meets required spectral resolution and readout noise.\n- The Arcus mission includes two co-aligned instruments working simultaneously.\n- The XRS Instrument Control Unit (XICU) controls the activities of the XRS.\n\n## Key Insights\n- The Arcus Probe mission concept provides a significant improvement in sensitivity and resolution over previous missions, enabling breakthrough science in understanding the universe.\n- The XRS instrument's design, including the use of two CCD focal planes and four parallel optical channels, allows for high-resolution spectroscopy and efficient detection of X-ray photons.\n- The CCDs used in the XRS instrument are designed and manufactured by MIT Lincoln Laboratory (MIT/LL), which has a proven track record of producing high-quality CCDs for space missions.\n- The laboratory performance results of the CCID-94 device demonstrate that it meets the required spectral resolution and readout noise for the Arcus mission, indicating that the instrument is capable of achieving its scientific goals.\n- The XRS Instrument Control Unit (XICU) plays a crucial role in controlling the activities of the XRS, including gathering and storing data, and processing event recognition.\n- The Arcus mission's use of two co-aligned instruments working simultaneously allows for a wide range of scientific investigations, including the study of time-domain science and the physics of time-dependent phenomena.\n- The high heritage MIT/LL CCDs used in the XRS focal plane provide a reliable and efficient means of detecting X-ray photons, enabling the instrument to achieve its scientific goals.",
                    "mindmap": "## Arcus Probe Mission Concept\n- Explores formation and evolution of clusters, galaxies, stars\n- High-resolution soft X-ray and UV spectroscopy\n- Agile response capability for time-domain science\n\n## X-Ray Spectrograph (XRS) Instrument\n- Two nearly identical CCD focal planes\n- Detects and records X-ray photons from dispersed spectra\n- Zero-order of critical angle transmission gratings\n\n## XRS Focal Plane Characteristics\n- Frametransfer X-ray CCDs\n- 8-CCD array per Detector Assembly\n- FWHM < 70 eV @ 0.5 keV\n- System read noise ≤ 4 e- RMS @ 625 kpixels/sec\n\n## Detector Assembly\n- Eight CCDs in a linear array\n- Tilted to match curved focal surface\n- Gaps minimized between CCDs\n- Alignment optimized with XRS optics\n\n## Detector Electronics\n- Programmable analog clock waveforms and biases\n- Low-noise analog signal processing and digitization\n- 1 second frame time for negligible pileup\n\n## XRS Instrument Control Unit (XICU)\n- Controls XRS activities and data transfer\n- Event Recognition Processor (ERP) extracts X-ray events\n- Reduces data rate by many orders of magnitude\n\n## CCD X-Ray Performance\n- Measured readout noise 2-3 e- RMS\n- Spectral resolution meets Arcus requirements\n- FWHM < 70 eV at 0.5 keV\n\n## CCID-94 Characteristics\n- Back-illuminated frame-transfer CCDs\n- 2048 × 1024 pixel imaging array\n- 24 × 24 µm image area pixel size\n- 50 µm detector thickness\n\n## Contamination Blocking Filter (CBF)\n- Protects detectors from molecular contamination\n- 45 nm polyimide + 30 nm Al\n- Maintained above +20°C by heater control\n\n## Optical Blocking Filter (OBF)\n- Attenuates visible/IR stray light\n- 40 nm Al on-chip filter\n- Works in conjunction with CBF"
                }
            }
        }
    if data['status'] != 'success':
        print('Failed to fetch data')
    else:
        for category, catdata in data.items():
            if category != 'status':
                for paper_id, paperdata in catdata.items():
                    title = paperdata['title']
                    category = paperdata['category']
                    summary = paperdata['summary']
                    mindmap = paperdata['mindmap']
                    citation = paperdata['citation']
                    access_key = os.getenv('ACCESS_KEY')
                    main(title, category, summary, mindmap, citation, access_key)