Unable to validate XML using schema, but works by reading file written from-Python Tutorial-php.cn

Home

Backend Development

Python Tutorial

Unable to validate XML using schema, but works by reading file written from

WBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWB

Feb 22, 2024 pm 12:34 PM

无法使用架构验证 XML，但可以通过从中读取写入的文件来工作

Question content

I am currently using lxml and want to validate the xml content.

I started from tei = etree.element("tei", nsmap={none: 'http://www.tei-c.org/ns/1.0'} Completely written in python, including Many child elements. p>

Now, I want to check if the structure is correct using a specific .xsd file using the following code:

xmlschema_doc = etree.parse(xsd_file_path)
xmlschema = etree.xmlschema(xmlschema_doc)
# run check
status = xmlschema.validate(xml_tree)

It returns false with error element 'tei': No matching global declaration available to verify root.

I observed a very strange thing, if I write xml using

et = etree.elementtree(xmldata)
et.write('test.xml', pretty_print=true, xml_declaration=true, encoding='utf-8')

If I reopen it with

b= etree.parse('test.xml') I end up with no errors, and as a result of xmlschema.validate(b), The xml structure is valid

Any idea what I need to add in the xml structure?

edit: Invalid first item in xml

The first item in a valid xml file

edit:

<?xml version='1.0' encoding='UTF-8'?>
<TEI xmlns="http://www.tei-c.org/ns/1.0">
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">article</title>
            <title xml:lang="fr">article</title>
            <title type="sub" xml:lang="en">A subtitle</title>
            <author role="aut">
              <persName>
                <forename type="first">John</forename>
                <surname>Doe</surname>
              </persName>
              <email>email</email>
              <idno type="http://orcid.org/">orcid</idno>
              <affiliation ref="#localStruct-affiliation"/>
              <affiliation ref="#struct-affiliation"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Jane</forename>
                <forename type="middle">Middle</forename>
                <surname>Doe</surname>
              </persName>
              <email>email</email>
              <idno type="http://orcid.org/">orcid</idno>
              <affiliation ref="#localStruct-affiliationA"/>
              <affiliation ref="#localStruct-affiliationB"/>
            </author>
          </titleStmt>
          <editionStmt>
            <edition>
              <ref type="file" subtype="author" n="1" target="upload.pdf"/>
            </edition>
          </editionStmt>
          <publicationStmt>
            <availability>
              <licence target="https://creativecommons.org/licenses//cc-by/"/>
            </availability>
          </publicationStmt>
          <notesStmt>
            <note type="audience" n="2"/>
            <note type="invited" n="1"/>
            <note type="popular" n="0"/>
            <note type="peer" n="1"/>
            <note type="proceedings" n="0"/>
            <note type="commentary">small comment</note>
            <note type="description">small description</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">article</title>
                <title xml:lang="fr">article</title>
                <title type="sub" xml:lang="en">A subtitle</title>
                <author role="aut">
                  <persName>
                    <forename type="first">John</forename>
                    <surname>Doe</surname>
                  </persName>
                  <email>email</email>
                  <idno type="http://orcid.org/">orcid</idno>
                  <affiliation ref="#localStruct-affiliation"/>
                  <affiliation ref="#struct-affiliation"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Jane</forename>
                    <forename type="middle">Middle</forename>
                    <surname>Doe</surname>
                  </persName>
                  <email>email</email>
                  <idno type="http://orcid.org/">orcid</idno>
                  <affiliation ref="#localStruct-affiliationA"/>
                  <affiliation ref="#localStruct-affiliationB"/>
                </author>
              </analytic>
              <monogr>
                <idno type="isbn">978-1725183483</idno>
                <idno type="halJournalId">117751</idno>
                <idno type="issn">xxx</idno>
                <imprint>
                  <publisher>springer</publisher>
                  <biblScope unit="serie">a special collection</biblScope>
                  <biblScope unit="volume">20</biblScope>
                  <biblScope unit="issue">1</biblScope>
                  <biblScope unit="pp">10-25</biblScope>
                  <date type="datePub">2024-01-01</date>
                </imprint>
              </monogr>
              <series/>
              <idno type="doi">reg</idno>
              <idno type="arxiv">ger</idno>
              <idno type="bibcode">erg</idno>
              <idno type="ird">greger</idno>
              <idno type="pubmed">greger</idno>
              <idno type="ads">gaergezg</idno>
              <idno type="pubmedcentral">gegzefdv</idno>
              <idno type="irstea">vvxc</idno>
              <idno type="sciencespo">gderg</idno>
              <idno type="oatao">gev</idno>
              <idno type="ensam">xcvcxv</idno>
              <idno type="prodinra">vxcv</idno>
              <ref type="publisher">https://publisher.com/ID</ref>
              <ref type="seeAlso">https://link1.com/ID</ref>
              <ref type="seeAlso">https://link2.com/ID</ref>
              <ref type="seeAlso">https://link3.com/ID</ref>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">keyword1</term>
                <term xml:lang="en">keyword2</term>
                <term xml:lang="fr">mot-clé1</term>
                <term xml:lang="fr">mot-clé2</term>
              </keywords>
              <classCode scheme="halDomain" n="physics"/>
              <classCode scheme="halDomain" n="halDomain2"/>
              <classCode scheme="halTypology" n="ART"/>
            </textClass>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="institution" xml:id="localStruct-affiliation">
          <orgName>laboratory for MC, university of Yeah</orgName>
          <orgName type="acronym">LMC</orgName>
          <desc>
            <address>
              <addrLine>Blue street 155, 552501 Olso, Norway</addrLine>
              <country key="LS">Lesotho</country>
            </address>
            <ref type="url" target="https://lmc.univ-yeah.com"/>
          </desc>
        </org>
        <org type="institution" xml:id="localStruct-affiliationB">
          <orgName>laboratory for MCL, university of Yeah</orgName>
          <orgName type="acronym">LMCL</orgName>
          <desc>
            <address>
              <addrLine>Blue street 155, 552501 Olso, Norway</addrLine>
              <country key="NO">Norway</country>
            </address>
            <ref type="url" target="https://lmcl.univ-yeah.com"/>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>

Correct answer

Look at

https://www.php.cn/link/e1ff36b97044a1c7c73c73e4d27aeba4, you should basically use

tei_namespace = "http://www.tei-c.org/ns/1.0"
tei = "{%s}" % tei_namespace

nsmap = {none : tei_namespace} # the default namespace (no prefix)

root = etree.element(tei + "tei", nsmap=nsmap) # lxml only!
text = etree.subelement(root, tei + "text")

And so on for all elements to ensure they are created in the tei namespace.

The elementtree created in memory valid for the schema (after I downloaded it with the imported w3c xml.xsd) is e.g.

from lxml import etree

TEI_NAMESPACE = "http://www.tei-c.org/ns/1.0"
TEI = "{%s}" % TEI_NAMESPACE

NSMAP = {None : TEI_NAMESPACE} # the default namespace (no prefix)

root = etree.Element(TEI + "TEI", nsmap=NSMAP) # lxml only!
text = etree.SubElement(root, TEI + "text")
body = etree.SubElement(text, TEI + "body")
listBibl = etree.SubElement(body, TEI + "listBibl")
biblFull = etree.SubElement(listBibl, TEI + "biblFull")
sourceDesc = etree.SubElement(biblFull, TEI + "sourceDesc")
profileDesc = etree.SubElement(biblFull, TEI + "profileDesc")


xmlschema_doc = etree.parse("aofr.xsd")
xmlschema = etree.XMLSchema(xmlschema_doc)
# run check
status = xmlschema.validate(root)

print(status)

print(xmlschema.error_log)

The above is the detailed content of Unable to validate XML using schema, but works by reading file written from. For more information, please follow other related articles on the PHP Chinese website!

Statement

This article is reproduced at:stackoverflow. If there is any infringement, please contact admin@php.cn delete

How to Use Python to Find the Zipf Distribution of a Text FileMar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML?Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

How to Perform Deep Learning with TensorFlow or PyTorch?Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Serialization and Deserialization of Python Objects: Part 1Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: StatisticsMar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

Professional Error Handling With PythonMar 04, 2025 am 10:58 AM

In this tutorial you'll learn how to handle error conditions in Python from a whole system point of view. Error handling is a critical aspect of design, and it crosses from the lowest levels (sometimes the hardware) all the way to the end users. If y

What are some popular Python libraries and their uses?Mar 21, 2025 pm 06:46 PM

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

Scraping Webpages in Python With Beautiful Soup: Search and DOM ModificationMar 08, 2025 am 10:36 AM

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

See all articles