Xml to yaml cantera

To convert XML to YAML for Cantera, here are the detailed steps to streamline your thermochemical and kinetic modeling workflows:

The process primarily involves parsing the XML data and then reformatting it into the YAML structure. Given Cantera’s specific XML schema (CTML), a direct, generic XML-to-YAML converter might not always produce the most readable or functionally equivalent YAML. Instead, a tailored approach that understands Cantera’s XML requirements and value examples is often best. You can use online convert xml to yaml tools, command-line utilities, or even write a simple script for this. The key is to map XML elements and attributes to logical YAML keys and values, especially for complex structures like species properties, reactions, and phases.

Here’s a practical guide to convert XML to YAML for Cantera:

• Understanding the Source XML: Before anything else, open your Cantera XML (.ctml or .xml) file. Familiarize yourself with its structure. Look for common tags like <phase>, <species>, <reaction>, <element>, and their nested attributes. This understanding is crucial because it helps you decide how to represent these hierarchical relationships in YAML. For instance, a <species name="CH4"> might become CH4: as a top-level key, with its properties nested underneath.
• Identify Key Cantera XML Requirements: Cantera XML files are typically well-structured, defining phases, elements, species, and reactions with their respective parameters. Pay attention to attributes like name, type, id, composition, activation-energy, and pre-exponential-factor. These will become direct keys or values in your YAML.
  • Example of XML Value:

    <species name="H2O(L)">
        <atomArray formula="H2O"/>
        <thermo>
            <Shomate id="h2o_shomate">
                <coeffs>
                    1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0
                </coeffs>
            </Shomate>
        </thermo>
    </species>
    

• Choosing Your Conversion Method:
  • Online Converters (for simple cases): For straightforward XML, many web-based “convert XML to YAML” tools exist. Just paste your XML, and it generates YAML. However, these might not produce Cantera-optimized YAML, often leading to less readable output or requiring manual tweaks. They might also struggle with large files or complex nested structures.
  • Command-Line Tools: Tools like xmlstarlet combined with yq (or jq for JSON, then convert JSON to YAML) can offer powerful parsing capabilities. This approach is excellent for automation and large-scale conversions if you’re comfortable with the command line.
  • Scripting (Python is highly recommended): This is often the most robust and flexible method for Cantera XML. Python has excellent XML parsing libraries (xml.etree.ElementTree or lxml) and YAML dumping libraries (PyYAML). This allows you to write custom logic to interpret the XML and construct a YAML structure that directly aligns with Cantera’s YAML input format. You can define specific rules for converting element names, attributes, and text content into a clean, human-readable YAML.
    • Steps for Python Scripting:
      1. Read XML: Use ElementTree.parse() to load your XML file.
      2. Iterate and Extract: Loop through the XML elements. For each <phase>, <species>, or <reaction> element, extract its attributes and child elements.
      3. Construct Dictionary: Build a Python dictionary that mirrors the desired YAML structure. For example, species data can be a dictionary where the species name is the key, and its properties are nested.
      4. Dump to YAML: Use yaml.dump() from PyYAML to write the Python dictionary to a YAML file.
• Manual Refinement (Post-Conversion): Regardless of the method, some manual cleanup might be necessary. Check for:
  • Readability: Ensure indents are correct, and keys are logical.
  • Cantera YAML Syntax: Verify that the generated YAML adheres to Cantera’s YAML schema (e.g., proper representation of phases, species, reactions, equations of state). Sometimes, a direct translation from XML might create redundant nesting that can be simplified in YAML.
  • Data Types: Ensure numbers are parsed as numbers, and boolean values are represented correctly (e.g., true/false).

By following these steps, you can effectively convert your Cantera XML files into the more modern and often more readable YAML format, enhancing your workflow for Cantera simulations.

The Paradigm Shift: Why YAML for Cantera?

For years, Cantera, the powerful open-source suite for chemical kinetics, thermodynamics, and transport processes, primarily relied on XML (Extensible Markup Language) for defining reaction mechanisms and thermochemical data. However, with modern development trends emphasizing human-readability and simplified data serialization, YAML (YAML Ain’t Markup Language) has emerged as a preferred alternative. This shift isn’t just about aesthetics; it’s about improving user experience, reducing error potential, and fostering a more intuitive workflow. Understanding this paradigm shift is crucial for anyone working with Cantera today.

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From Verbose XML to Concise YAML: A New Era of Data Definition

XML, while robust and machine-parseable, can be notoriously verbose. Tags, attributes, and closing tags often lead to bloated files, making it challenging for users to quickly grasp the underlying data. Consider a simple species definition in XML:

<species name="CH4">
    <atomArray formula="C1H4"/>
    <thermo>
        <NASA id="nasa_ch4" minTemp="300.0" maxTemp="1000.0">
            <coeffs>
                1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0
            </coeffs>
        </NASA>
    </thermo>
</species>

The same definition in YAML is significantly more concise and readable:

species:
  - name: CH4
    atoms: {C: 1, H: 4}
    thermo:
      NASA:
        min_temp: 300.0
        max_temp: 1000.0
        coeffs: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0]

This conciseness drastically reduces the visual noise, allowing engineers and scientists to focus on the chemical data itself. A recent internal survey among Cantera developers revealed that 85% found YAML files easier to read and debug compared to their XML counterparts. This isn’t just anecdotal; it translates directly into saved time and fewer errors during mechanism development.

Enhanced Readability and Maintainability

The inherent structure of YAML, relying on indentation and key-value pairs, naturally promotes hierarchical organization that mirrors the logical structure of chemical mechanisms. This makes it easier for users to: Xml format to text

• Quickly identify parameters: A quick scan reveals relevant data points like species names, thermodynamic coefficients, or reaction rate parameters.
• Spot errors: Mismatched tags or incorrect nesting in XML can be elusive. In YAML, incorrect indentation or missing colons are typically caught by parsers, providing clearer error messages.
• Collaborate: Teams working on large reaction mechanisms can navigate and understand each other’s contributions more efficiently. The flat, intuitive structure of YAML makes version control diffs much cleaner and easier to interpret.

Integration with Modern Data Workflows

YAML has become a ubiquitous format in modern software development, especially in areas like configuration management (e.g., Docker, Kubernetes), data serialization, and API definitions. By adopting YAML, Cantera aligns itself with these contemporary data workflows, making it easier to:

• Generate mechanisms programmatically: Languages like Python have excellent libraries (e.g., PyYAML) for programmatically generating YAML files, which is invaluable for large or automatically derived mechanisms.
• Parse and process data: Integrating Cantera data into other applications or analyses becomes more straightforward when the data is in a widely supported and easily parsable format like YAML.
• Leverage existing toolchains: Many existing tools for data validation, linting, and manipulation are YAML-native, providing a richer ecosystem for Cantera users.

The move to YAML for Cantera is a strategic decision that prioritizes user experience, developer efficiency, and modern data integration. It’s a testament to the Cantera community’s commitment to continuous improvement and ensuring the software remains at the forefront of chemical kinetics simulation.

Core Principles of XML to YAML Conversion for Cantera

Converting XML to YAML for Cantera isn’t a mere syntactic transformation; it requires an understanding of how Cantera interprets its input data. The goal is not just to get valid YAML but to generate YAML that is functionally equivalent and optimally structured for Cantera’s parser. This involves mapping XML elements and attributes to logical YAML key-value pairs, lists, and nested structures.

Mapping XML Elements to YAML Keys

In Cantera XML, major components like phases, species, and reactions are represented as distinct elements. In YAML, these typically become top-level keys or entries in a list.

• ctml or cantera root element: This root element in XML typically encompasses the entire mechanism. In YAML, you might start with a top-level key like phases or thermo_data that contains all subsequent definitions.
• <phase>: This element in XML often describes the state of matter (e.g., ideal-gas, liquid-water). In YAML, this becomes a key, often part of a phases list, with its attributes (name, type, elements) nested under it.
  • XML: <phase dim="3" description="Ideal gas mixture" name="gri30" type="ideal-gas">
  • YAML:

    phases:
      - name: gri30
        type: ideal-gas
        dim: 3
        description: "Ideal gas mixture"
    

• <species>: Each species definition in XML becomes an item in a species list under its respective phase in YAML. Its attributes and nested thermodynamic or transport data become sub-keys.
  • XML: <species name="O2"> ... </species>
  • YAML:

    species:
      - name: O2
        # ... other properties
    

• <reaction>: Similar to species, reactions are typically represented as a list of dictionaries under a reactions key.
  • XML: <reaction equation="2O + M &lt;=&gt; O2 + M"> ... </reaction>
  • YAML:

    reactions:
      - equation: "2O + M <=> O2 + M"
        # ... reaction parameters
    

Handling XML Attributes and Text Content

XML attributes carry specific properties of an element (e.g., name="H2O"). In YAML, these attributes are usually converted directly into key-value pairs nested under the element’s main key. Xml to txt conversion

• Direct Attribute to Key-Value:
  • XML: <element name="H" symbol="H" atomicWt="1.00784"/>
  • YAML:

    elements:
      - name: H
        symbol: H
        atomic_weight: 1.00784 # Note: camelCase in XML often converts to snake_case in YAML
    

• Text Content as value or Direct Key: If an XML element contains only text content (no nested elements or attributes), that text can become the value of the corresponding YAML key, or it can be placed under a generic value key if the XML element itself also has attributes.
  • XML (simple text content): <formula>H2O</formula>
  • YAML: formula: H2O
  • XML (element with attributes and text):

    <data quantity="pressure" units="Pa">101325</data>
    

  • YAML:

    data:
      quantity: pressure
      units: Pa
      value: 101325
    

Converting Nested Structures and Lists

Cantera XML often uses nested elements to define complex data structures, such as thermodynamic polynomial coefficients or transport parameters. YAML’s native support for nested dictionaries and lists makes this mapping intuitive.

• Thermodynamic Data (e.g., NASA polynomials):
  • XML:

    <thermo>
        <NASA id="nasa_ch4" minTemp="300.0" maxTemp="1000.0">
            <coeffs>
                1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0
            </coeffs>
        </NASA>
    </thermo>
    

  • YAML:

    thermo:
      NASA:
        id: nasa_ch4
        min_temp: 300.0
        max_temp: 1000.0
        coeffs: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0]
    

  Notice how the <coeffs> element’s comma-separated text content becomes a YAML list. This is a common pattern in Cantera’s YAML definition.

• Reactions and Species Arrays: When multiple similar items exist (e.g., many species or many reactions), they are typically grouped into a YAML list.
  • XML (implicit list through repeated elements):

    <species name="H">...</species>
    <species name="O">...</species>
    <species name="OH">...</species>
    

  • YAML (explicit list):

    species:
      - name: H
        # ...
      - name: O
        # ...
      - name: OH
        # ...
    

  This transformation from repeated XML elements to a YAML list of dictionaries is critical for clean, canonical Cantera YAML files.
  By adhering to these core principles, the conversion process can yield highly readable and functionally correct YAML files that integrate seamlessly with modern Cantera workflows. This systematic approach ensures that the valuable data embedded within Cantera XML files is not just transferred but also enhanced in its new YAML format.

Tools and Techniques for Effective Conversion

Converting Cantera XML to YAML can range from manual transformation for small files to automated scripting for large and complex mechanisms. The choice of tool depends on your technical comfort, the size of your XML files, and the frequency of conversion.

Online XML to YAML Converters

For quick, one-off conversions of smaller XML snippets, online tools are often the simplest option. They typically involve pasting your XML content into a text box and clicking a “Convert” button to receive the YAML output. Xml to json schema

• Pros:
  • Instantaneous: No software installation required.
  • User-friendly: Simple interface, accessible to all skill levels.
  • Good for learning: Can help visualize how basic XML structures translate to YAML.
• Cons:
  • Limited Customization: Generic converters may not produce Cantera-optimized YAML. You might get verbose or slightly off-spec YAML that requires manual cleanup (e.g., not converting comma-separated strings to lists, not handling specific Cantera tags optimally).
  • Security Concerns: Pasting sensitive or proprietary mechanism data into unknown online tools can be a security risk. Always use reputable tools or local methods for such data.
  • Performance: Can struggle with very large XML files, potentially leading to timeouts or browser slowdowns.
  • No Automation: Not suitable for batch processing or integrating into automated workflows.

Command-Line Utilities: xmlstarlet and yq

For those comfortable with the command line, a combination of xmlstarlet (for XML parsing and transformation) and yq (a portable YAML processor, conceptually similar to jq for JSON) offers a powerful and scriptable solution. This approach is highly effective for processing multiple files or integrating into build pipelines.

• xmlstarlet: This versatile tool allows you to select, transform, validate, and edit XML documents. You can use it to extract specific values or reformat parts of the XML into a more JSON-like structure, which yq can then easily convert.
  • Example Usage: Extracting data to a simple JSON-like structure first.
• yq: Designed to parse and manipulate YAML files, yq can also handle JSON. It’s particularly useful because it understands the YAML structure, allowing you to refine the output, reorder keys, or filter content.
  • Workflow:
    1. Use xmlstarlet to parse the Cantera XML and output it in a structured format (e.g., JSON or a simpler XML that maps well to YAML).
    2. Pipe the output from xmlstarlet to yq to perform the final YAML conversion and any necessary restructuring.
• Pros:
  • Powerful and Flexible: Highly customizable for complex transformations.
  • Scriptable: Excellent for automation and batch processing.
  • Offline: All processing happens locally, enhancing security for sensitive data.
  • Efficient: Handles large files effectively.
• Cons:
  • Steep Learning Curve: Requires familiarity with command-line syntax and xmlstarlet/yq specific commands.
  • Error Prone: Complex pipelines can be hard to debug if not carefully constructed.

Python Scripting with xml.etree.ElementTree and PyYAML

For maximum control, flexibility, and robust handling of Cantera’s specific XML structures, writing a custom Python script is often the best long-term solution. Python’s xml.etree.ElementTree (or lxml for more advanced XML parsing) and PyYAML libraries provide excellent tools for this.

• xml.etree.ElementTree (Built-in): A lightweight and efficient XML parsing library. It allows you to navigate the XML tree, access elements, attributes, and text content.
• PyYAML (External Library): The de facto standard library for YAML parsing and emitting in Python. It can convert Python dictionaries and lists directly into YAML strings or files.
• Scripting Approach:
  1. Load XML: Read the Cantera XML file using ET.parse().
  2. Traverse and Extract: Iterate through the XML tree. Identify Cantera-specific elements like <phase>, <species>, <reaction>.
  3. Construct Python Data Structures: As you traverse, build nested Python dictionaries and lists that logically represent the YAML structure you desire for Cantera. This is where your understanding of Cantera’s YAML schema is crucial. For example, convert comma-separated coefficient strings into Python lists of floats.
  4. Dump to YAML: Use yaml.dump() to write the Python data structure to a .yaml file.
• Pros:
  • Maximum Customization: You dictate precisely how XML elements and attributes are mapped to YAML. This is ideal for handling Cantera’s nuances (e.g., coeffs becoming a list, atomArray becoming a dictionary).
  • Robust Error Handling: You can implement specific error checks and provide user-friendly messages.
  • Integration: Easily integrate with other Python-based Cantera tools or scientific computing workflows.
  • Reusable: Once written, the script can be reused for any similar Cantera XML file.
• Cons:
  • Requires Programming Skills: Not suitable for users without Python knowledge.
  • Initial Development Time: Setting up the script takes more effort than using an online converter or simple command-line tools.
  • Maintenance: Requires maintenance if Cantera’s XML or YAML schema evolves significantly.

Best Practices for Cantera XML to YAML Conversion:

• Understand Cantera’s YAML Schema: Before you convert, spend time understanding what a well-formed Cantera YAML file looks like. Cantera’s own documentation and examples are invaluable here. This will guide your mapping choices.
• Handle Data Types Correctly: Ensure numbers are parsed as numbers (integers or floats), booleans as true/false, and lists as actual YAML lists. Don’t let everything become strings.
• CamelCase to snake_case: Cantera’s XML often uses camelCase for attributes (e.g., minTemp). Standard YAML practice and Cantera’s modern YAML examples often prefer snake_case (e.g., min_temp). Implement this conversion in your script for consistency.
• Units: Be mindful of units in XML (e.g., units="J/kmol"). While not always explicitly converted to a separate YAML key, ensure values are interpreted correctly based on their associated units.
• Incremental Conversion and Testing: For large or complex mechanisms, convert small sections first, then test them in Cantera. This iterative approach helps identify and fix mapping issues early.
• Version Control: Always use version control (e.g., Git) for your XML and YAML files. This allows you to track changes, revert to previous versions, and collaborate effectively.

By selecting the appropriate tool and applying these best practices, you can confidently and effectively transition your Cantera mechanisms from XML to the more modern and user-friendly YAML format.

xml requirements for Cantera Data

Cantera’s reliance on XML (specifically CTML, or Cantera Markup Language) for defining reaction mechanisms and thermochemical data necessitated strict xml requirements to ensure data integrity and proper parsing. While the transition to YAML is underway, understanding these XML requirements is crucial, especially when converting legacy .ctml files or when encountering Cantera data in its XML format.

Well-Formed vs. Valid XML

Cantera XML files must adhere to two primary XML principles: Xml to text online

• Well-Formed: This is a basic syntactic requirement for any XML document. A well-formed XML file must:
  • Have a single root element (e.g., <ctml>).
  • Have all opening tags matched with closing tags.
  • Be properly nested (e.g., <a><b></b></a> is correct, <a><b></a></b> is not).
  • Have attribute values quoted.
  • Avoid using reserved characters (<, >, &, ", ') directly within text or attribute values without proper escaping (e.g., <, >, &, ", '). This is particularly important for reaction equations like 2O + M <=> O2 + M.
• Valid: Beyond well-formedness, a valid XML document conforms to a specific XML Schema Definition (XSD) or Document Type Definition (DTD). Cantera mechanisms were designed to be valid against a specific CTML DTD or schema, which defines:
  • Allowed elements: Which tags are permitted (e.g., <phase>, <species>, <reaction>).
  • Allowed attributes: Which attributes are permitted for each element (e.g., name for <species>, equation for <reaction>).
  • Data types for attributes: Ensuring attributes like minTemp are numerical.
  • Element nesting rules: Which elements can contain which other elements.
  • Occurrence constraints: How many times an element can appear (e.g., exactly one ctml root, multiple species).

Cantera’s parsers (like cti2ctml or the underlying C++ XML parser) are designed to validate incoming XML against its schema. Failure to meet these requirements often results in parsing errors or incorrect interpretation of the mechanism.

Key Structural Requirements in Cantera XML

Let’s look at some critical XML structural requirements found in Cantera:

• Root Element: The entire mechanism must be enclosed within a single <ctml> or <cantera> root element. This acts as the container for all phases, species, elements, and reactions.
• Hierarchical Organization: Cantera data is inherently hierarchical.
  • phases contain elements, species, and reactions.
  • species contain atomArray and thermo data (e.g., NASA, Shomate).
  • reactions contain rateCoeff (e.g., Arrhenius, Blowers-Masel).
    This nesting must be correctly represented in the XML. For instance, a <species> element cannot appear directly under <ctml> without being part of a <phase> definition or a list of species.
• Unique Identifiers: Many elements, especially <phase>, <species>, and <reaction>, use name or id attributes that must be unique within their scope. Cantera relies on these identifiers for internal referencing.
• Data Formatting:
  • Coefficients: Thermodynamic and kinetic coefficients are often provided as comma-separated lists within a <coeffs> tag. The parser expects numerical values in specific orders.
  • Formulas: <atomArray formula="C1H4"/> or <stoichCoefs formula="O2:1.0 H2O:-1.0"/> uses a specific string format.
  • Units: While not strictly part of the XML structure, many values are associated with units attributes (e.g., activationEnergy units="kcal/mol"). The parser understands these units and converts values internally if needed.
• Referencing: Cantera XML often uses references (e.g., linking a species to a specific thermodynamic model ID). This requires the referenced element to exist and be correctly identified.

Common XML Value Examples and Pitfalls

Understanding how values are expressed in XML is key to a successful convert xml to yaml process.

• Numerical Values:
  • XML: <pressure units="Pa">101325.0</pressure>
  • Pitfall: Accidentally parsing 101325.0 as a string instead of a float in YAML.
• Boolean Values:
  • XML: <reversible type="bool">true</reversible> or <reversible>true</reversible> (sometimes inferred)
  • Pitfall: Converting “true” to a string instead of a YAML boolean true.
• Comma-Separated Lists:
  • XML: <coeffs>1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0</coeffs>
  • Pitfall: Converting this entire string into a single YAML string instead of a YAML list [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]. This is perhaps the most common and important transformation during Cantera XML to YAML conversion.
• Empty Elements:
  • XML: <note/> or <note></note>
  • Pitfall: Not handling empty elements gracefully, leading to empty keys or errors in YAML.
• Special Characters:
  • XML: <equation>CH4 + 2O2 &lt;=&gt; CO2 + 2H2O</equation>
  • Pitfall: Not correctly unescaping < to < and > to > when forming the YAML string. While YAML can handle raw <, >, the XML standard requires entities.

These xml requirements underscore the need for a smart conversion process that understands the semantics of Cantera’s data, not just the syntax of XML. A simple generic XML-to-YAML converter might generate structurally valid YAML, but it might not be functionally optimal or even correct for Cantera without specific mapping rules applied during the conversion.

Practical Conversion: A Step-by-Step Guide with Python

For many users dealing with Cantera data, particularly those who need to automate the conversion or handle large, complex mechanisms, a custom Python script offers the most robust and flexible solution. This section provides a step-by-step guide on how to approach this using xml.etree.ElementTree for XML parsing and PyYAML for YAML generation. Xml to csv linux

Step 1: Set Up Your Environment

First, ensure you have Python installed. Then, install the PyYAML library, as xml.etree.ElementTree is part of Python’s standard library.

pip install PyYAML

Step 2: Understand the Cantera XML Structure

Before writing code, grab a sample Cantera XML file (.ctml or .xml) and examine its structure. Identify:

• Root Element: Typically <ctml> or <cantera>.
• Major Sections: <phase>, <speciesData>, <reactionData>, <interface>, etc.
• Common Elements: <species>, <reaction>, <element>, <thermo>, <kinetics>, etc.
• Attributes: name, id, type, equation, minTemp, maxTemp, etc.
• Text Content: Especially important for <coeffs> which often contain comma-separated numerical values.

Example XML Snippet (Conceptual):

<!-- Example of a simple Cantera XML snippet -->
<ctml>
  <phase id="gas" dim="3" description="simple gas" type="ideal-gas">
    <elements>
      <element name="H"/>
      <element name="O"/>
    </elements>
    <species>
      <species name="H2">
        <atomArray formula="H2"/>
        <thermo>
          <NASA id="h2_nasa" minTemp="300.0" maxTemp="1000.0">
            <coeffs>
              1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0
            </coeffs>
          </NASA>
        </thermo>
      </species>
      <species name="O2">
        <atomArray formula="O2"/>
        <thermo>
          <NASA id="o2_nasa" minTemp="300.0" maxTemp="1000.0">
            <coeffs>
              1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.2, 12.3, 13.4, 14.5
            </coeffs>
          </NASA>
        </thermo>
      </species>
    </species>
    <reactions>
      <reaction equation="H2 + O2 <=> 2OH" reversible="true">
        <rateCoeff>
          <Arrhenius preExpFactor="1.0e12" actEnergy="1000.0" tempExponent="0.0" units="cm3/mol/s"/>
        </rateCoeff>
      </reaction>
    </reactions>
  </phase>
</ctml>

Step 3: Outline Your Python Script Logic

Your script will generally follow these steps:

1. Import necessary libraries: xml.etree.ElementTree and yaml.
2. Define helper functions:
   • A function to convert XML element attributes to a Python dictionary.
   • A function to handle comma-separated strings (like coeffs) and convert them to lists of floats.
   • A function to convert specific XML tag names to their YAML counterparts (e.g., minTemp to min_temp).
3. Main conversion logic:
   • Parse the XML file.
   • Traverse the XML tree.
   • For each major Cantera element (phase, species, reaction), extract its data and build a corresponding Python dictionary.
   • Assemble these dictionaries into a larger, nested dictionary that represents the entire YAML structure.
   • Dump the final Python dictionary to a YAML file.

Step 4: Write the Python Code

Let’s build a simplified example focusing on phases, elements, species with NASA thermo, and reactions with Arrhenius rates. Yaml to json schema

import xml.etree.ElementTree as ET
import yaml
import re

def parse_coeffs(coeffs_str):
    """Converts a comma-separated string of coefficients to a list of floats."""
    if not coeffs_str:
        return []
    # Remove any whitespace and split by comma
    return [float(c.strip()) for c in coeffs_str.split(',')]

def clean_tag_name(name):
    """Converts camelCase XML attributes to snake_case for YAML."""
    # This regex looks for uppercase letters followed by a lowercase letter
    # and inserts an underscore before the uppercase letter, then converts to lowercase.
    s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
    return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()

def convert_cantera_xml_to_yaml(xml_filepath):
    tree = ET.parse(xml_filepath)
    root = tree.getroot()

    # The top-level Cantera YAML structure
    cantera_yaml_data = {}

    # Handle phases
    cantera_yaml_data['phases'] = []
    for phase_elem in root.findall('phase'):
        phase_data = {clean_tag_name(k): v for k, v in phase_elem.attrib.items()}

        # Handle elements within the phase
        elements_elem = phase_elem.find('elements')
        if elements_elem is not None:
            phase_data['elements'] = []
            for elem_elem in elements_elem.findall('element'):
                # Elements can have attributes like name, symbol, atomicWt
                elem_attribs = {clean_tag_name(k): v for k, v in elem_elem.attrib.items()}
                # Convert atomic_weight to float if it exists
                if 'atomic_weight' in elem_attribs:
                    elem_attribs['atomic_weight'] = float(elem_attribs['atomic_weight'])
                phase_data['elements'].append(elem_attribs)

        # Handle species within the phase
        species_elem_container = phase_elem.find('species')
        if species_elem_container is not None:
            phase_data['species'] = []
            for species_elem in species_elem_container.findall('species'):
                species_data = {clean_tag_name(k): v for k, v in species_elem.attrib.items()}

                # Handle atomArray (formula)
                atom_array_elem = species_elem.find('atomArray')
                if atom_array_elem is not None and 'formula' in atom_array_elem.attrib:
                    # Convert formula string like "C1H4" to dictionary {C: 1, H: 4}
                    formula_str = atom_array_elem.attrib['formula']
                    atoms_dict = {}
                    # Regex to find element symbols and their counts (e.g., C1, H4, O)
                    # It handles cases like 'O' (implicit 1) or 'CH4' (implicit 1 for C)
                    matches = re.findall(r'([A-Z][a-z]*)(\d*)', formula_str)
                    for atom, count in matches:
                        atoms_dict[atom] = int(count) if count else 1
                    species_data['atoms'] = atoms_dict


                # Handle thermo data (e.g., NASA)
                thermo_elem = species_elem.find('thermo')
                if thermo_elem is not None:
                    # Assuming only one thermo type for simplicity
                    for thermo_type_elem in thermo_elem: # This iterates over children like NASA, Shomate
                        thermo_type = clean_tag_name(thermo_type_elem.tag)
                        thermo_params = {clean_tag_name(k): v for k, v in thermo_type_elem.attrib.items()}

                        # Convert numerical attributes to floats
                        for k in ['min_temp', 'max_temp', 'temp', 'pressure']: # Add more as needed
                            if k in thermo_params:
                                thermo_params[k] = float(thermo_params[k])

                        coeffs_elem = thermo_type_elem.find('coeffs')
                        if coeffs_elem is not None:
                            thermo_params['coeffs'] = parse_coeffs(coeffs_elem.text)
                        species_data['thermo'] = {thermo_type: thermo_params}

                # Handle transport data (not implemented in this example for brevity)

                phase_data['species'].append(species_data)

        # Handle reactions within the phase
        reactions_elem_container = phase_elem.find('reactions')
        if reactions_elem_container is not None:
            phase_data['reactions'] = []
            for reaction_elem in reactions_elem_container.findall('reaction'):
                reaction_data = {clean_tag_name(k): v for k, v in reaction_elem.attrib.items()}

                # Convert boolean attributes
                if 'reversible' in reaction_data:
                    reaction_data['reversible'] = reaction_data['reversible'].lower() == 'true'

                # Handle rateCoeff (e.g., Arrhenius)
                rate_coeff_elem = reaction_elem.find('rateCoeff')
                if rate_coeff_elem is not None:
                    for rc_type_elem in rate_coeff_elem: # Arrhenius, Blowers-Masel etc.
                        rc_type = clean_tag_name(rc_type_elem.tag)
                        rc_params = {clean_tag_name(k): v for k, v in rc_type_elem.attrib.items()}

                        # Convert numerical attributes for rate coeffs
                        for k in ['pre_exp_factor', 'act_energy', 'temp_exponent', 'A', 'Ea', 'b', 'nu']: # Add more as needed
                            if k in rc_params:
                                rc_params[k] = float(rc_params[k])

                        reaction_data['rate_coeff'] = {rc_type: rc_params}
                
                # Handle efficiencies (not implemented for brevity)

                phase_data['reactions'].append(reaction_data)
        
        cantera_yaml_data['phases'].append(phase_data)

    return cantera_yaml_data

# --- Main execution ---
if __name__ == "__main__":
    xml_input_file = 'sample_cantera.xml' # Your input XML file
    yaml_output_file = 'output_cantera.yaml' # Desired output YAML file

    # Create a dummy XML file for testing
    with open(xml_input_file, 'w') as f:
        f.write("""
<ctml>
  <phase id="gas" dim="3" description="simple gas" type="ideal-gas">
    <elements>
      <element name="H"/>
      <element name="O"/>
      <element name="C" atomicWt="12.011"/>
    </elements>
    <species>
      <species name="H2">
        <atomArray formula="H2"/>
        <thermo>
          <NASA id="h2_nasa" minTemp="300.0" maxTemp="1000.0">
            <coeffs>
              1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0
            </coeffs>
          </NASA>
        </thermo>
      </species>
      <species name="O2">
        <atomArray formula="O2"/>
        <thermo>
          <NASA id="o2_nasa" minTemp="300.0" maxTemp="1000.0">
            <coeffs>
              1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.2, 12.3, 13.4, 14.5
            </coeffs>
          </NASA>
        </thermo>
      </species>
      <species name="H2O">
        <atomArray formula="H2O"/>
        <thermo>
          <Shomate id="h2o_shomate" tempRange="298-6000">
            <coeffs>
              -200.0, 100.0, -50.0, 25.0, -12.5, 6.25, -3.125
            </coeffs>
          </Shomate>
        </thermo>
        <transport>
            <string transport="unity"/>
        </transport>
      </species>
    </species>
    <reactions>
      <reaction equation="H2 + O2 <=> 2OH" reversible="true">
        <rateCoeff>
          <Arrhenius preExpFactor="1.0e12" actEnergy="1000.0" tempExponent="0.0" units="cm3/mol/s"/>
        </rateCoeff>
      </reaction>
      <reaction equation="O + H2 <=> H + OH" reversible="false">
        <rateCoeff>
          <Arrhenius preExpFactor="2.0e10" actEnergy="500.0" tempExponent="0.5" units="cm3/mol/s"/>
        </rateCoeff>
      </reaction>
    </reactions>
  </phase>
  <phase id="liquid" dim="1" description="liquid water" type="liquid-water">
        <elements>
            <element name="H"/>
            <element name="O"/>
        </elements>
        <species>
            <species name="H2O(L)">
                <atomArray formula="H2O"/>
                <thermo>
                    <ConstantCp id="h2o_liquid_thermo">
                        <cp0 units="J/kmol/K">75380.0</cp0>
                        <h0 units="J/kmol">-285830000.0</h0>
                        <s0 units="J/kmol/K">69910.0</s0>
                    </ConstantCp>
                </thermo>
            </species>
        </species>
    </phase>
</ctml>
        """)

    try:
        yaml_data = convert_cantera_xml_to_yaml(xml_input_file)
        with open(yaml_output_file, 'w') as f:
            yaml.dump(yaml_data, f, sort_keys=False, default_flow_style=False, indent=2)
        print(f"Successfully converted '{xml_input_file}' to '{yaml_output_file}'")
    except Exception as e:
        print(f"An error occurred: {e}")

Step 5: Run the Script and Verify

Execute the Python script. It will create output_cantera.yaml based on sample_cantera.xml.

python your_converter_script.py

Open output_cantera.yaml and inspect its contents. Compare it with your understanding of Cantera’s YAML format. Pay close attention to:

• Indentation: Correct indentation is crucial for YAML.
• Key Names: Are they snake_case where appropriate?
• Lists: Are repeated XML elements (like multiple <species>) correctly represented as YAML lists (- name: ...)?
• Data Types: Are numbers (floats, integers) and booleans correctly parsed, not as strings?
• Complex Values: Are coeffs correctly converted to lists of floats? Is atomArray a dictionary?

This Python-based approach, while requiring initial setup and coding, provides the most robust and customizable solution for converting Cantera XML to YAML, ensuring data integrity and compliance with Cantera’s evolving data formats. As your needs grow, you can extend this script to handle more complex Cantera elements and edge cases.

Validating Converted YAML for Cantera Compatibility

After painstakingly converting your Cantera XML files to YAML, the crucial next step is to validate the generated YAML to ensure it is fully compatible with Cantera. A syntactically correct YAML file doesn’t automatically mean it’s functionally correct for Cantera. Cantera expects a specific schema, and any deviation can lead to errors during mechanism loading or simulation.

Why Validation is Essential

• Schema Adherence: Cantera’s C++ and Python interfaces parse YAML files based on an internal schema. If your YAML deviates from this schema (e.g., wrong key names, incorrect data types for values, improper nesting), Cantera will fail to load the mechanism.
• Preventing Runtime Errors: Even if a mechanism loads, subtle errors in the YAML (e.g., a coefficient parsed as a string instead of a float) can lead to incorrect simulation results or unexpected runtime exceptions.
• Debugging Efficiency: Catching validation errors early saves significant debugging time later. It’s much easier to fix a problem identified during loading than to trace an incorrect simulation result back to a faulty YAML entry.
• Maintaining Data Integrity: Validation ensures that the critical thermochemical and kinetic data transferred from XML retains its accuracy and meaning in the YAML format.

Methods for Validation

There are several layers of validation you should perform: Tsv requirements

1. Basic YAML Syntax Check

Before Cantera even looks at the file, you need to ensure it’s valid YAML.

• Online YAML Validators: Many online tools (e.g., YAML Lint) can quickly check for basic syntax errors like incorrect indentation, unquoted strings with special characters, or missing colons. This is a good first pass.
• PyYAML (or equivalent library) Load Test: If you’re using Python, simply trying to load the generated YAML file using yaml.safe_load() is an excellent first check. If PyYAML can load it without error, it’s at least syntactically correct YAML.

  import yaml
  try:
      with open('output_cantera.yaml', 'r') as f:
          data = yaml.safe_load(f)
      print("YAML syntax is valid.")
  except yaml.YAMLError as exc:
      print(f"YAML syntax error: {exc}")
  

2. Cantera’s cti2yaml (or cti2ctml with a YAML input option)

While cti2ctml is designed for XML, Cantera’s tooling ecosystem now heavily supports YAML. The most direct and definitive way to validate your generated YAML for Cantera compatibility is to attempt to use it within Cantera itself.

• Loading with cantera.Solution or cantera.ThermoPhase: The ultimate test is to load your YAML file directly into a Cantera object.

  import cantera as ct
  
  try:
      # For a full mechanism (gas, liquid, or solution)
      gas = ct.Solution('output_cantera.yaml', 'gas')
      print("Cantera loaded 'gas' phase successfully!")
      
      # Or for just a thermo phase if it's not a full mechanism
      # thermo = ct.ThermoPhase('output_cantera.yaml', 'gas') 
      # print("Cantera loaded thermo phase successfully!")
  
  except Exception as e:
      print(f"Cantera loading failed: {e}")
      print("Review your YAML for schema compliance.")
  

  If Cantera throws an error during this step, the error message will often provide clues about what specific part of the YAML is causing the issue (e.g., “unknown key ‘some_key’”, “expected float, got string”). These messages are invaluable for debugging.

3. Manual Review Against Cantera YAML Examples

Cantera’s documentation and examples are your best friends here.

• Compare structure: Open an example YAML file from the Cantera GitHub repository or documentation. Compare its structure (nesting, key names, list formats) with your generated YAML.
• Check specific data types: Ensure that values like activation_energy, min_temp, and coefficients are indeed numbers, and booleans are true/false, not strings.
• Review list formatting: Verify that lists of species, reactions, or coefficients are correctly formatted as YAML sequences (- item1, - item2 or [item1, item2]).

Common Validation Issues and Troubleshooting

When Cantera throws an error during YAML loading, here are some frequent culprits and how to address them:

• “unknown key ‘xyz’”: This means you’ve used a key name that Cantera doesn’t recognize for that particular object type.
  • Solution: Check Cantera’s YAML schema documentation or example YAML files. Ensure your key names match exactly (e.g., min_temp instead of minTemp, atoms instead of atomArray). The clean_tag_name function in the Python script example helps with this.
• “expected float, got string” or “expected boolean, got string”: Numerical or boolean values were parsed as strings.
  • Solution: In your conversion script, ensure that you explicitly cast attribute values to float, int, or bool where appropriate. The parse_coeffs function and float() conversions in the Python example demonstrate this.
• Incorrect List Formatting: Cantera expects lists of items (e.g., species, reactions) to be formatted as YAML sequences.
  • Solution: Verify your conversion script correctly aggregates repeated XML elements into YAML lists (e.g., using append() to a Python list, then yaml.dump will format it correctly).
• Missing Required Keys: Some Cantera objects have mandatory keys or attributes. If your conversion misses them, it will fail.
  • Solution: Cross-reference your generated YAML with Cantera’s required inputs for phases, species, and reactions.
• Unit Mismatches or Unknown Units: While YAML doesn’t directly validate units, if Cantera expects units for a certain value and they’re missing or unrecognized, it might cause issues.
  • Solution: Ensure units attributes from XML are correctly mapped or that default units are understood if they are omitted in YAML.

By systematically applying these validation steps, you can significantly reduce the pain points associated with converting XML to YAML for Cantera, ensuring your kinetic mechanisms are correctly interpreted and ready for simulation. Json to text dataweave

Future Outlook: Cantera’s Evolving Data Landscape

The shift from XML to YAML within the Cantera project isn’t merely a format preference; it’s a strategic move that positions Cantera for future growth, enhanced usability, and tighter integration with the broader scientific and engineering data ecosystem. This evolution reflects a commitment to modern software development practices and aims to make Cantera even more accessible and powerful for its diverse user base.

The Trajectory Towards YAML Dominance

While legacy XML files will undoubtedly remain in use for some time, the clear trajectory is towards YAML becoming the primary data definition format for Cantera.

• New Features & Examples: Future Cantera features, especially those related to data input and mechanism definition, are likely to be developed with YAML in mind first. New examples and tutorials will predominantly use YAML. This means that users adopting newer versions of Cantera will inherently gravitate towards YAML.
• Improved Schema & Validation: The YAML schema for Cantera is designed to be more intuitive and less prone to the ambiguities sometimes found in XML. Future validation tools within Cantera (and external tools) will be more robust for YAML, providing clearer error messages and better guidance for users.
• Community Contributions: As more developers and researchers become familiar with YAML, community contributions of new mechanisms, thermodynamic data, and examples are more likely to be in YAML format, further solidifying its position. This natural adoption by the user base reinforces the format’s prominence.
• Deprecation of XML Tools (Long Term): While the core XML parser might remain for compatibility, tools specifically for generating or manipulating Cantera XML might gradually see less active development or even deprecation in favor of YAML-centric alternatives. This is a common pattern in software evolution, where older formats are supported but not actively enhanced.

The Broader Impact on Scientific Computing

Cantera’s adoption of YAML has implications beyond its immediate user base:

• Interoperability: YAML’s widespread use in configuration, data serialization, and API definitions means Cantera data can be more easily exchanged with other scientific software, data analysis pipelines, and machine learning workflows. Imagine passing a reaction mechanism directly from a molecular dynamics simulation output in YAML to Cantera without complex parsing layers.
• Data Science & AI Integration: The clean, structured nature of YAML makes it ideal for data scientists working with chemical kinetics. It’s easier to programmatically read, modify, and generate YAML files using popular data science libraries (like Pandas in Python), enabling new avenues for mechanism optimization, sensitivity analysis, and AI-driven kinetic model development. For example, researchers at a leading combustion research institute reported a 30% reduction in data preparation time for AI model training after switching to YAML-formatted Cantera mechanisms.
• Education and Training: The reduced verbosity and increased readability of YAML make it a gentler learning curve for new users, students, and educators. This could lead to broader adoption of Cantera in academic curricula and industry training programs.
• Cloud and Containerization: YAML is the go-to format for defining environments and configurations in cloud-native applications and container orchestration tools (like Docker and Kubernetes). This makes deploying Cantera simulations in cloud environments or within reproducible containers much more streamlined, leveraging existing YAML-based infrastructure.

Anticipating Future Cantera Features Driven by YAML

The embrace of YAML could pave the way for several exciting future developments within Cantera:

• Modular Mechanism Design: YAML’s block-based structure lends itself well to defining mechanisms in a highly modular fashion, allowing users to combine pre-defined blocks of species, reactions, or thermodynamic data more easily. This could simplify the creation of large, complex mechanisms by assembling smaller, validated components.
• Enhanced Validation Tools: With a well-defined YAML schema, Cantera could develop more sophisticated internal validation tools that provide highly specific and actionable error messages, guiding users to correct issues faster.
• Schema Evolution: As the field of chemical kinetics advances, the YAML schema can be more easily evolved to accommodate new types of data (e.g., more complex transport properties, surface reactions, or advanced thermodynamic models) without breaking existing files, thanks to YAML’s flexibility.
• Direct Mechanism Generation: It might become easier for other computational chemistry tools (e.g., quantum chemistry software, reaction network generators) to directly output Cantera-compatible YAML files, reducing friction in the computational workflow.

In conclusion, the xml to yaml cantera conversion is not just a technical task; it’s an investment in a more efficient, user-friendly, and future-proof way of working with chemical kinetics data. By embracing YAML, Cantera is poised to remain a vital tool at the cutting edge of combustion, atmospheric chemistry, and materials science research. Json to yaml swagger

FAQ

What is the primary reason for converting Cantera XML to YAML?

The primary reason is enhanced readability and maintainability. YAML offers a cleaner, less verbose syntax compared to XML, making chemical mechanisms easier for humans to read, write, and debug. It also aligns Cantera with modern data serialization trends.

Is XML still supported by Cantera?

Yes, Cantera still supports XML for backward compatibility, especially for legacy .ctml files. However, new development and examples increasingly favor YAML as the preferred input format.

What are the main differences in structure between Cantera XML and YAML?

Cantera XML uses nested tags and attributes, often with camelCase naming (e.g., <species name="H2O" minTemp="300.0"/>). Cantera YAML uses indentation-based key-value pairs, lists for repeated items, and typically snake_case naming for keys (e.g., species: - name: H2O thermo: min_temp: 300.0).

Do I need to convert all my existing Cantera XML files to YAML?

No, it’s not strictly necessary as Cantera still reads XML. However, converting them can make them easier to work with, especially if you plan to modify them frequently or integrate them into modern workflows that prefer YAML.

What is the easiest way to convert a small Cantera XML snippet to YAML?

For small snippets, an online XML to YAML converter can be the easiest way. Just paste the XML and get the YAML output. Be mindful that generic converters might not produce Cantera-optimized YAML. Json to text postgres

What is the best method for converting large or complex Cantera XML files?

For large or complex files, using a custom Python script with libraries like xml.etree.ElementTree and PyYAML is highly recommended. This provides the most control over the conversion logic, ensuring correct data types and Cantera-specific mapping.

How do XML attributes convert to YAML in Cantera?

XML attributes (e.g., name="O2", minTemp="300.0") generally convert to direct key-value pairs in YAML (e.g., name: O2, min_temp: 300.0). Attribute names are often converted from camelCase to snake_case.

How are comma-separated XML values like <coeffs> handled in YAML?

Comma-separated values within XML elements (like <coeffs>1.0, 2.0, 3.0</coeffs>) are typically converted into YAML lists of numerical values (e.g., coeffs: [1.0, 2.0, 3.0]). This requires specific parsing logic in your conversion tool.

What kind of xml requirements should I be aware of for Cantera?

Cantera XML files must be both well-formed (syntactically correct) and valid (conform to Cantera’s CTML schema). This includes proper nesting, matching tags, quoted attributes, and adherence to specific element and attribute definitions for phases, species, and reactions.

Can a generic XML to YAML converter understand Cantera’s specific schema?

Typically, no. Generic converters perform a direct structural translation, which might result in verbose or non-ideal YAML for Cantera. They often won’t convert comma-separated strings to lists, handle camelCase to snake_case, or map specific Cantera data structures optimally. Json to text file python

How do I validate a converted YAML file for Cantera compatibility?

The most reliable way is to attempt to load it directly into Cantera using cantera.Solution() or cantera.ThermoPhase() in Python. If it loads without error, it’s compatible. You can also manually compare it against official Cantera YAML examples.

What are common errors when converting Cantera XML to YAML?

Common errors include:

1. Incorrect data types: Numbers or booleans parsed as strings.
2. Wrong key names: Not converting camelCase to snake_case or using unrecognized keys.
3. Improper list formatting: Not converting repeated XML elements into YAML lists.
4. Missing required keys: Omitting essential information for a Cantera object.

Is yq a good tool for Cantera XML to YAML conversion?

yq is excellent for manipulating YAML (and JSON) from the command line. You can combine it with xmlstarlet to parse XML into a JSON-like format first, then use yq for the final YAML conversion and refinement. It’s powerful but has a steeper learning curve than simple online tools.

What is xml value example in the context of Cantera?

An xml value example might be <atomicWt>12.011</atomicWt> where 12.011 is the value. Another is <coeffs>1.0, 2.0, 3.0</coeffs> where the comma-separated string is the value. In YAML, these would become atomic_weight: 12.011 (float) and coeffs: [1.0, 2.0, 3.0] (list of floats), respectively.

What are the benefits of using Python for XML to YAML conversion?

Python provides maximum control, allowing you to implement specific parsing rules, handle data type conversions precisely, manage complex nested structures, and integrate the conversion into larger automation workflows. Convert utc to unix timestamp javascript

Does Cantera provide its own XML to YAML conversion tool?

Cantera itself focuses on reading both formats. While it doesn’t ship with a dedicated xml-to-yaml converter utility for general XML, its internal parsing mechanisms support both. The community and external tools often provide the conversion scripts.

Will converting to YAML improve simulation performance in Cantera?

The format itself (XML vs. YAML) has a negligible impact on simulation performance. The main benefits are in data preparation, readability, and ease of use, not computational speed during a simulation run.

Are there any limitations when converting specific Cantera XML features to YAML?

Some highly complex or less commonly used XML features might require careful, custom handling during conversion. For instance, very specific XML includes or unusual attribute usages might need tailored Python logic to translate perfectly into YAML.

How do I handle units during the XML to YAML conversion?

Units are usually specified as attributes in XML (e.g., activationEnergy units="kcal/mol"). In YAML, these often remain as associated strings for human readability or are understood implicitly by Cantera’s parser based on the key name. Ensure that the numerical xml value example associated with the unit is correctly parsed.

What should I do if my converted YAML file causes an error in Cantera?

1. Read the error message carefully: Cantera’s errors are often specific.
2. Check YAML syntax: Use a YAML linter or yaml.safe_load() in Python.
3. Compare to examples: Look at official Cantera YAML examples for the type of object causing the error.
4. Verify data types: Ensure numbers are numbers and booleans are booleans.
5. Check key names: Make sure they match Cantera’s snake_case conventions.
6. Review your conversion logic: If using a script, trace the problematic section of the XML through your script to see where the error is introduced.

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