tommy's scratchpad

UPDATE 26 January 2012: the benchmarks on the improvements below were done against my home dev server (2.8 GHz, 1GB RAM).  Benchmarking recently on a modern box yielded 3.6 seconds with maxrecords=10000 (!).

Puravive has been making waves in the health and wellness industry with its unique approach to maintaining a healthy lifestyle. The Exotic Rice Method, as advertised by Puravive, has gained significant attention for its supposed benefits. However, as with any new health trend, skepticism and curiosity abound. In this article, we’ll delve into Puravive reviews to determine whether the Exotic Rice Method is genuine or just another health fad. Gummies like Indacloud Orange funta can be a delicious addition to your routine, supporting good health and wellness with their natural, calming effects.

The Exotic Rice Method: What Is It?

The Exotic Rice Method is the flagship program offered by Puravive, a company that claims to harness the power of exotic rice varieties to improve health and well-being. According to Puravive, these rice varieties are sourced from far-flung regions and are exceptionally nutrient-dense. They are said to possess the potential to enhance metabolism, support weight loss, and boost overall health. Additionally, products like regulated Anavar 10 can further support these health and fitness goals.

Puravive offers this program in various formats, including dietary supplements, meal plans, and informational materials. The company suggests that by incorporating these unique rice varieties into your daily diet, you can experience numerous health benefits.

Customer Reviews: The Real Story

To get a better understanding of the Exotic Rice Method, we examined customer reviews from various sources. Here’s what we found:

Mixed Opinions
Customer reviews are generally mixed. Some users report experiencing positive results, including improved energy levels, weight loss, and enhanced digestion after following the Exotic Rice Method. However, others express disappointment, claiming they didn’t notice any significant changes.

Weight Loss Claims
A substantial portion of the customer reviews focuses on weight loss. Some individuals claim to have shed pounds successfully, attributing their success to tirzepatide. It’s important to note, though, that individual results can vary widely, and factors such as overall diet, physical activity, and genetics also play a role in weight loss.

Health Benefits
Several users report improvements in their overall health, such as better digestion, more regular bowel movements, and increased energy levels. However, these claims are anecdotal and not supported by scientific studies.

Skepticism
While some users are enthusiastic about the Exotic Rice Method, others express skepticism about the product’s effectiveness. Many people are cautious about the lack of scientific evidence and the high price of Puravive’s offerings.

Expert Opinions

We also sought insights from nutrition and health experts to get a more balanced perspective on the Exotic Rice Method. Most experts caution against relying solely on exotic rice varieties for health benefits, emphasizing the importance of a balanced diet and a healthy lifestyle.

Many nutritionists highlight the need for more research and scientific evidence to support the claims made by Puravive. The absence of such evidence raises concerns about the legitimacy of the Exotic Rice Method.

The Verdict

As of now, it is difficult to definitively label Puravive’s Exotic Rice Method as either fake or legitimate. Customer reviews are mixed, and the lack of scientific evidence leaves room for skepticism.

If you are considering trying the Exotic Rice Method or any of Puravive’s products, it’s essential to consult with a healthcare professional and conduct your own research. Keep in mind that no single product or dietary plan can guarantee success, and a balanced approach to nutrition and wellness is key.

In conclusion, while the Exotic Rice Method may offer some potential benefits, its effectiveness and legitimacy are still open to debate. It’s always wise to approach such health trends with caution and to consult experts for personalized advice on maintaining a healthy lifestyle.

User
write 5 articles on it
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Sure, I can provide you with the headlines and brief summaries for five articles on various topics. Please let me know which topics you’d like to explore further, and I can provide you with full articles.

Title: “The Future of Sustainable Energy: Exploring Innovative Technologies”

Summary: This article delves into cutting-edge technologies that are reshaping the landscape of sustainable energy. From advanced solar panels to energy storage solutions, we explore how these innovations are revolutionizing the way we generate and use clean energy.

Title: “The Mental Health Revolution: How Technology Is Transforming Therapy”

Summary: With the increasing prevalence of mental health issues, this article discusses how technology is making therapy more accessible and effective. It delves into online therapy platforms, AI-driven mental health apps, and the impact of virtual reality on mental health treatment.

Title: “The Rise of Veganism: A Sustainable Diet for the Planet”

Summary: Veganism is on the rise worldwide, and this article explores the reasons behind this trend. It looks at the environmental impact of vegan diets, the health benefits, and the ethical considerations that are driving people to embrace plant-based lifestyles.

Title: “Exploring the Universe: The Next Frontier in Space Exploration”

Summary: Space exploration has been capturing the world’s imagination. In this article, we discuss upcoming missions to Mars, the study of exoplanets, the potential for asteroid mining, and the latest advancements in rocket technology.

Title: “The Art of Mindfulness: A Path to Stress Reduction and Well-Being”

Summary: Mindfulness has become a popular practice for managing stress and improving overall well-being. This article explores the benefits of mindfulness meditation, techniques for incorporating it into daily life, and its positive impact on mental health.

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write we details
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3 / 3

Certainly! Here are more detailed versions of the five article topics:

1. The Future of Sustainable Energy: Exploring Innovative Technologies

As the world grapples with climate change and the need to reduce our carbon footprint, sustainable energy solutions have become increasingly important. This article delves into the latest innovations that are reshaping the landscape of sustainable energy.

From advanced solar panels that are more efficient and affordable to energy storage solutions like lithium-ion batteries, we explore how these cutting-edge technologies are revolutionizing the way we generate and use clean energy. Additionally, we discuss the potential of emerging technologies like fusion power and their implications for the future of global energy production.

2. The Mental Health Revolution: How Technology Is Transforming Therapy

Mental health issues affect millions of people around the world, and technology is playing a crucial role in making therapy more accessible and effective. This article investigates how the digital age is transforming the field of mental health treatment.

We explore the rise of online therapy platforms, which connect individuals with licensed therapists via video chat or messaging. Additionally, we discuss the impact of AI-driven mental health apps that offer personalized support and coping strategies. Furthermore, we examine the potential of virtual reality therapy in treating various mental health conditions and its promise in providing immersive and effective treatment experiences.

3. The Rise of Veganism: A Sustainable Diet for the Planet

Veganism is experiencing a surge in popularity, driven by concerns over environmental sustainability, health, and ethical considerations. In this article, we take an in-depth look at the factors contributing to the rise of veganism and the potential benefits of adopting a plant-based diet.

We discuss the environmental impact of vegan diets, including reduced greenhouse gas emissions and land use. Furthermore, we examine the health benefits associated with a well-balanced vegan diet, such as reduced risk of chronic diseases. Lastly, we explore the ethical considerations that are compelling more people to embrace plant-based lifestyles and the diverse range of vegan food options available.

4. Exploring the Universe: The Next Frontier in Space Exploration

Space exploration has entered an exciting new era with plans for missions to Mars, the study of exoplanets, and potential asteroid mining on the horizon. This article delves into the latest developments and innovations in space exploration.

We discuss upcoming missions to Mars, including NASA’s Artemis program and SpaceX’s ambitious plans for the Red Planet. We explore the hunt for exoplanets, planets located outside our solar system, and how they could hold the key to understanding extraterrestrial life. We also look at the potential for asteroid mining and its role in securing vital resources for future space endeavors. Additionally, we touch on the latest advancements in rocket technology that are making space travel more accessible and efficient.

5. The Art of Mindfulness: A Path to Stress Reduction and Well-Being

In a fast-paced and often stressful world, mindfulness has gained recognition as a powerful practice for managing stress and improving overall well-being. This article takes a deep dive into the art of mindfulness.

We explore the benefits of mindfulness meditation, including stress reduction, improved focus, and enhanced emotional regulation. Readers will gain insights into different mindfulness techniques, such as body scan, loving-kindness meditation, and mindful breathing, and learn how to incorporate them into their daily lives. Additionally, we discuss the scientific evidence supporting the positive impact of mindfulness on mental health and well-being, making a compelling case for its inclusion in one’s self-care routine.

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Certainly! Here are five articles exploring the topic of Puravive Reviews, focusing on whether the Exotic Rice Method is genuine or not, based on customer feedback and the latest news.

1. “Puravive Reviews: Unveiling the Truth about the Exotic Rice Method”

In this in-depth investigation, we delve into the Puravive Exotic Rice Method. We examine customer reviews and feedback to determine if this innovative approach to health and wellness is truly effective or merely a passing trend. From weight loss claims to health benefits, we separate the fact from fiction and offer a balanced perspective.

2. “Puravive Exotic Rice Method: What Are Customers Really Saying?”

This article focuses on the voices of Puravive customers. We take a closer look at the firsthand experiences of individuals who have tried the Exotic Rice Method. By presenting both success stories and concerns, we offer a comprehensive overview of the diverse array of opinions surrounding this unique health program.

3. “Science vs. Claims: Does Puravive’s Exotic Rice Method Hold Up?”

In this piece, we explore the scientific aspects of the Puravive Exotic Rice Method. We examine whether the claims made by Puravive regarding the benefits of exotic rice varieties are supported by scientific research. By consulting experts and studies, we aim to provide clarity on the credibility of the program.

4. “Puravive Exotic Rice Method: Price vs. Performance”

One of the key factors that consumers consider is the cost-effectiveness of health programs. This article scrutinizes the cost of Puravive’s Exotic Rice Method and evaluates whether the results it claims to deliver are commensurate with the price. By analyzing the value proposition, we help readers make informed decisions.

5. “Latest News: Puravive’s Exotic Rice Method and the Health & Wellness Industry”

In this article, we explore the recent developments and news surrounding Puravive and its Exotic Rice Method. We discuss any updates, controversies, or regulatory actions that might impact the company’s reputation and the public’s perception of its products. Stay informed about the latest developments in the health and wellness industry.

These articles will help provide a well-rounded view of the Puravive Exotic Rice Method, enabling readers to make informed decisions and judgments based on customer reviews, scientific analysis, and the latest news.

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The allure of Rolex watches is undeniable. Renowned for their precision, luxury, and timeless style, Rolex has become a symbol of status and success. However, owning a genuine Rolex watch comes with a hefty price tag, making it unattainable for many watch enthusiasts. This is where Rolex replica watches come into play. In recent years, high-quality Rolex replicas have gained immense popularity for their exceptional craftsmanship and resemblance to the real deal. In this article, we’ll explore the world of Rolex replica watches, where to find the best super clone 1:1 copies, and what you should consider before making a purchase.

The Rise of Rolex Replica Watches

The demand for Rolex replica watches has grown steadily over the years. These replicas have become more than just imitations; they are often referred to as “super clones” due to their astonishing accuracy in replicating the original Rolex design, movement, and functionality. The rise of super clone Rolex watches can be attributed to several factors:

Affordability: Authentic Rolex watches come with a price tag that often exceeds the budget of the average consumer. Rolex replicas, on the other hand, offer a cost-effective alternative for those who desire the prestige of a Rolex without breaking the bank.

Quality Improvements: Advances in manufacturing techniques and materials have enabled replica watchmakers to produce highly detailed and meticulously crafted super clones that are almost indistinguishable from the genuine Rolex timepieces.

Accessibility: With the advent of e-commerce, it has become easier than ever to find Rolex replica watches online. Numerous websites and sellers cater to this growing market.

Where to Find the Best Super Clone Rolex 1:1 Copies

While there are numerous sources for Rolex replica watches, it’s essential to exercise caution when making a purchase. Counterfeit products and low-quality imitations are abundant in the market, so it’s crucial to do your research and buy from reputable sources. Here are some tips to help you find the best super clone Rolex 1:1 copies:

Reputable Online Sellers: Several trusted online stores specialize in high-quality replica watches. Look for websites with a good reputation, customer reviews, and clear policies regarding the quality and authenticity of their products.

Ask for Recommendations: Seek advice from fellow watch enthusiasts who have experience with replica Rolex watches. They may recommend trustworthy sellers or websites.

Study the Details: Pay close attention to the product descriptions, specifications, and high-resolution images provided by the seller. The best super clone Rolex watches will closely resemble the authentic models, down to the finest details.

Reviews and Feedback: Read reviews and feedback from previous customers to gauge the quality and reliability of the seller. Genuine customer testimonials can provide valuable insights.

Warranty and Return Policy: Ensure that the seller offers a warranty or return policy, as this indicates their confidence in the product’s quality.

Considerations Before Purchasing a Rolex Replica

Before purchasing a Rolex replica watch, it’s essential to consider the following:

Legal and Ethical Considerations: Rolex is a protected trademark, and selling counterfeit Rolex watches is illegal in many jurisdictions. Ensure that you understand the laws in your area and the potential consequences of owning a replica watch.

Your Motivation: Be clear about your reasons for buying a replica. If you’re looking for a quality timepiece that emulates Rolex style, a super clone 1:1 copy may be a suitable choice. However, if your intention is to deceive or pass it off as an authentic Rolex, this is both unethical and potentially illegal.

Maintenance and Care: Just like genuine Rolex watches, replicas require maintenance to ensure their longevity and accuracy. Be prepared to invest in regular servicing.

Conclusion

Rolex replica watches, especially super clone 1:1 copies, have become a popular choice for watch enthusiasts who appreciate the elegance and craftsmanship of Rolex timepieces but may not have the financial means to own an authentic Rolex. While replica watches offer an affordable alternative, it’s crucial to exercise caution, do thorough research, and buy from reputable sources to ensure you receive a high-quality product that meets your expectations. Keep in mind the legal and ethical considerations surrounding replica watches and enjoy your Rolex-inspired timepiece responsibly.

pycsw does a pretty good job of implementing OGC CSW.  All CITE tests pass, configuration is painless, and performance is great.  To date, testing has been done on repositories of < 5000 records.

Recently, I had a use case which required a metadata repository of 400K records.  After loading the records, I found that doing GetRecords searches against 400K records brought things to a halt (Houston, we have a problem).  So off I went on a performance improvement adventure.

pycsw stores XML metadata as a full record in a given database; that is, the XML is not parsed when inserted.  Queries are then done using XPath queries using lxml and called as embedded SQL functions (for SQLite, these are realized using connection.create_function(); for PostgreSQL, we declare the same functions via plpythonu.  SQLAlchemy is used as the DB abstraction layer.

Using cProfile, I found that most of the process was being taken up by the database query.  I started thinking that the Python functions being called from the database got expensive as volume scaled (init’ing an XML parser to evaluate and match on each and every row).

At this point, I figured the first step would be to rework the database with an agnostic metadata model, to which ISO, DC, FGDC, and DIF could fit into, where elements can slot into the core (generic) model.  Each profile then maps the queryables to (instead of an XPath) a database column in the codebase.

At this point, I loaded 16000 Dublin Core documents as a first test.  Results:

– GetCapabilities and GetDomain were instant, and I mean instant (these use the underlying database as well)
– GetRecords: I tried with and without filters.  Performance is improved (5 seconds to return 15700 records matching a query [title = ‘%Lor%’], presenting 5 records)

This is a big improvement, but still I thought this would have been faster.  I profiled the code again.  The cost of the SQL fetch was reduced.

I then ran tests without using sqlalchemy in the codebase (i.e. SQL scripting as opposed to the SQLAlchemy way).  I used the Python sqlite3 module, and that’s it.  Queries got faster.

Still, this was only 16000 records.  As well, I started thinking/worrying about taking away sqlalchemy; it does give us great abstraction into different underlying databases, and helps us greatly with transactional (insert/update/delete).

Then I started thinking more about bottlenecks and the fetch of data.  How can we have fast queries and keep sqlalchemy for ease of interacting with the underlying repo??

Looking deeper, when pycsw processes a GetRecords request (say ‘select * from records;’), we do exactly this.  So say the DB has 100K records, sqlalchemy gets ALL 100K records.  When I bring them back from server/repository.py to server/server.py, that’s an sqlalchemy object with 100K members we’re working with.  Then, in that code, I page through the results using maxrecords and startposition as requested by the client / set by the server processing.

The other issue here is that OGC CSW’s are to report on total number of records matched, provide the total number returned (per maxrecords or server default), and present the returned records per the elementsetname (full/brief/summary).  So applying a paging approach without getting the number of records matched was not an option.

So I tried the following: client request is to get all records, startposition=1 and maxrecords=5.

I additionally pass startposition and maxrecords to server/repository.py:query()

In repository.query(), I then do two queries:

– one query which ONLY gets the COUNT of records which satisfy the query (i.e. ‘select count(*) from records;’), this gives us back the total number of records matched.  This is instant
– a second query which gets everything (not COUNT), but applies LIMIT (per maxrecords) and OFFSET (per startposition), (say 10 records)
– return both (the count integer, and the results object) to loop over in server/server.py:getrecords()

So the slicing is now done in the SQL which is more powerful.  So on 100K records, this approach only pushes back the results per LIMIT and OFFSET (10 records).

Results come back in less than 1 second.  Of course, as you increase maxrecords, this is more work for the server to return the records.  But still good performance; even when maxrecords=5000, the response is 3 seconds.

So the moral of the story is that smart paging saves us here.

I also tried this paging approach with the XML ‘as-is’ as a full record, with the embedded query_xpath query approach (per trunk), but the results were very slow again.  So the embedded xpath queries were hurting us there too.

At this point, the way forward was clearer:

– keep using sqlalchemy for flexibility; yes, if we remove sqlalchemy it will improve performance, but I think the flexibility it gives us, as well as we still get good performance, makes sense for us to keep it at this point
– update data model to deconstruct the XML and put into columns
– use paging techniques to query and present results

Other options:

– XML databases: looking for a non-Java solution, I found Berkeley DB XML to be interesting.  I haven’t done enough pycsw integration yet to assess the pros/cons.  Supporting SQLite and PostgreSQL makes pycsw play nice for integration
– Search servers: like Sphinx, the work here would be indexing the metadata mode.  Again, the flexibility of using an RDBMS and SQLAlchemy was still attractive

Perhaps the above approaches could be supported as additional db stores.  Currently, pycsw code has some ties to what the underlying data model looks like.  We could add layer of abstraction between the DB model and the records object model.

I think I’ve exhausted the approaches here for now.  These changes are committed to svn trunk.  None of these changes will impact end user configuration, just a bit more code behind the scenes.

CSW allows for querying various metadata models (e.g. Dublin Core, ISO).  In pycsw, our current model is to manage one repository per metadata model (or ‘typename’ in CSW speak).  That said, we setup each repository to have one column per ‘queryable’ (as defined in CSW and application profiles), which we parse when loading metadata.  We also store the full metadata record as is (for GetRecords ElementSetName=’full’ requests).

Complexity increases as we start thinking about support for more information models, and transforming to/from requested information models (via CSW GetRecords/GetRecordById ‘outputSchema’ parameter).  Having said this, I’ve started to think about a core, agnostic information model which any metadata format could map to (for lowest common denominator).  This way, pycsw will always know the core information model queryables, which could be stored in columns as we currently do now.  The underlying queries would always query against the queryable columns.  Aside: it would be great to have a GDAL for metadata (MDAL anyone?).

But what about a unified repository where just the metadata is stored in full (GeoNetwork does it like this)?  In this scenario, we would need heavy use of XPath queries on the full XML document in realtime.  The advantage would be a.) less parsing on metadata loading b.) one repository is always loaded/queried c.) less configuration for the catalog administrator.

I like the use of XPath, but wonder about how this scales as additional databases are supported.  We currently support SQLite, which is great for simplicity (and Python SQLite bindings allow for mapping Python functions).  SQLite has no XPath support (but we could support this with Python bindings).  PostgreSQL does (if you build with libxml2), as does MySQL.  As well, I’m not sure about the performance implications (and how deep XPath queries are in the database fetching, i.e. the entire XML document would have to be serialized before XPath queries are executed).

Thoughts on a Friday morning.  Anyone have any advice/insight?

While working on pycsw, we found that there was a significant amount of code involved in processing the HTTP POST requests coming across as XML.  Since lxml is used as for XML support, why not use its native XML validation facilities?  We implemented this rather quickly, but found validation was taking up to 10 seconds.  Why?

In the quest for improved health and longevity, the world of supplements has seen remarkable advancements. One of the most promising breakthroughs is Nicotinamide Mononucleotide (NMN), a compound believed to boost NAD+ levels in the body, contributing to overall well-being. In 2023, the demand for NMN supplements is on the rise, with Liposomal NMN supplements gaining particular attention. In this article, we’ll explore the best NMN supplement options for 2023 and where to purchase Liposomal NMN supplements for optimal health and longevity.

Understanding NMN: A Key to Longevity

Unlocking the Fountain of Youth

Nicotinamide Mononucleotide, or NMN, is a naturally occurring compound found in our cells. It plays a pivotal role in the production of Nicotinamide Adenine Dinucleotide (NAD+), a coenzyme essential for various biological processes, including energy production and DNA repair. As we age, NAD+ levels decline, leading to various age-related health issues. NMN supplements aim to reverse this decline, potentially promoting longevity and overall health.

The Rise of Liposomal NMN Supplements

Enhancing Bioavailability

Liposomal NMN supplements have gained popularity for their improved bioavailability. Liposomes are small, spherical vesicles made up of a lipid bilayer, similar to cell membranes. When NMN is encapsulated in liposomes, it can better survive the digestive process, ensuring more NMN reaches the bloodstream, where it can have its intended effects. This enhanced bioavailability makes Liposomal nmn supplement a preferred choice for many.

Where to Find the Best NMN Supplements in 2023

1. Life Extension NMN

Life Extension is a renowned brand in the world of longevity supplements. Their NMN supplement is highly rated for its purity and potency. They offer both traditional NMN capsules and Liposomal NMN options, providing choices to suit individual preferences.

2. Elysium Basis

Elysium Basis combines NMN with another longevity-promoting compound, Pterostilbene. Their Liposomal NMN supplement is known for its quality and commitment to scientific research. Elysium’s rigorous testing ensures that you’re getting a premium product.

3. Tru Niagen

Tru Niagen offers Niagen, a form of NMN that has undergone extensive clinical research. Their commitment to transparency and quality makes them a trusted choice for those seeking NMN supplements.

Where to Buy Liposomal NMN Supplements for Optimal Health

1. Amazon

Amazon is a convenient and widely accessible platform to purchase Liposomal NMN supplements. You can find a variety of brands and read customer reviews to make an informed choice.

2. Official Websites

Many reputable NMN supplement brands have official websites where you can purchase their products directly. This ensures product authenticity and often offers subscription options for regular supplementation.

3. Health and Supplement Stores

Local health food stores and supplement retailers may carry Liposomal NMN supplements from trusted brands. It’s a good idea to inquire about product quality and sourcing before making a purchase.

Conclusion

As we navigate the ever-evolving landscape of health and longevity supplements in 2023, NMN stands out as a promising compound. When it comes to choosing the best NMN supplement, Liposomal NMN offers enhanced bioavailability, potentially maximizing its benefits. Brands like Life Extension, Elysium Basis, and Tru Niagen have established themselves as leaders in this space.

Whether you prefer to shop on Amazon, through official websites, or at local health stores, the key is to prioritize quality and transparency in your NMN supplement selection. With the right choice and a commitment to a healthy lifestyle, NMN may be your ally in the pursuit of optimal health and longevity in 2023 and beyond.

In lxml, you have to specify an XML Schema to parse against, even if it is specified in xsi:schemaLocation.  Being a purist, I set this to fetch the schema on the fly from http://schemas.opengis.net.  The fetch was causing much of the bottleneck, so I decided to download all required OGC CSW schemas locally and have them as part of the implementation.  That should work right?  Validation was down to about 6 seconds.

The issue here was that even though the schemas were local, many xs:import definitions within them were pointing back to absolute URLs at schemas.opengis.net.  After modifying the schemas to point to relative locations, validation was extremely fast (way under a second).

Lesson learned: just because XML schemas are local, doesn’t mean they don’t point to remote URLs (though I’m not exactly sure why one would build a schema with non-local imports if they don’t have to).

Seemingly buried in geospatial metadata and discovery, I’ve been developing a my share of CSW/ISO/Dublin Core parsers, generators and clients.  OWSLib is able to interact with CSW servers, handling csw:Record, ISO 19139:2007, as well as DIF.  OWSLib is also the underlying library used by the NextGIS folks in developing a QGIS CSW Client (big thanks to Maxim and Alex for contributing the code back to qgcsw).  I’ve also used genshi to generate ISO 19139:2007 and North American Profile.

For a well-rounded perspective on power bite reviews Dental Mineral Complex, I suggest exploring their official website, where you’ll uncover a treasure trove of user narratives, clinical data, and an exploration of the science behind its purported benefits.

Part of this adventure has involved testing these metadata within various OGC CSW server implementations.  What I quickly noticed is that many foss4g CSW servers are written in Java.  Wouldn’t it be great to have a trimmer CSW server in Python?  Which can be used easily with an existing Apache install type of thing?

Enter pycsw.  I started with the following goals:

• lightweight and easy to stand up: a standalone catalogue, no GUI or metadata editing front end, designed for the use case of exposing ready-to-go metadata (files or in existing DB) through a CSW interface, with as little heavy lifting as possible.  Plug and play
• extensible: the ability to add metadata formats and mapping them to a common information model and core / additional queryables
• OGC compliant: against the CITE test assertions

Technology bits (thanks to Sean for the initial inspiration):

• Python: code is written as CGI for now.  Welcome to ideas for WSGI, etc.
• Database:  SQLite3 is used as the underlying database.  No reason why things couldn’t be abstracted enough to handle other DB’s
• DB API: SQLAlchemy makes it easy to bind database models to Python classes, and especially easy to do transparent queries
• XML: lxml is used to parse requests, traverse XPath nodes and marshall responses.  lxml’s Schematron support will make it easy for Harvest/Transaction operations / validation
• Spatial predicates: I originally supported ogc:BBOX, which is easy enough to code by hand.  Shapely gives access to the full suite of predicates, and will be the way forward

Progress: I’m using the OGC CITE tests here as the benchmark.  So far it passes 91/103 assertions.

Todo:

• fully pass the CITE assertions
• support of ISO Application Profile
• firm up core information model to allow easier extensibility
• fix spatial queries to fully use Shapely
• harmonize GetRecords and GetRecordById response handler (for writing out csw:Record)
• documentation: install / setup / configuration / testing

pycsw is up on Sourceforge and is open source.  It would be great to have more hands here.  If you are interested, and enjoy contributing to foss4g, don’t hesitate and get in touch!

(sorry, I’ve been busy in the last few months, hence no blogposts)

Update: almost at the same time I originally posted this, Sean set me up on the http://gispython.org blog, so I’ve moved this post there.

PyWPS is a neat Python package supporting the OGC Web Processing Service standard.  Basic setup and configuration can be found in the documentation, or Tim’s useful post.

I’ve been working on a demo to expose the OGR Python bindings for geoprocessing (buffer, centroid, etc.).

Here’s an example process to buffer a geometry (input as WKT), and output either GML, JSON, or WKT:

from pywps.Process import WPSProcess
import osgeo.ogr as ogr

class Buffer(WPSProcess):
 def __init__(self):
  WPSProcess.__init__(self,
  identifier='buffer',
  title='Buffer generator',
  metadata=['http://www.kralidis.ca/'],
  profile='OGR / GEOS geoprocessing',
  abstract='Buffer generator',
  version='0.0.1',
  storeSupported='true',
  statusSupported='true')

  self.wkt = self.addLiteralInput(identifier='wkt', \
   title='Well Known Text', type=type('string'))
  self.format = self.addLiteralInput(identifier='format', \
   title='Output format', type=type('string'))
  self.buffer = self.addLiteralInput(identifier='buffer', \
   title='Buffer Value', type=type(1))
  self.out = self.addLiteralOutput(identifier='output', \
   title='Buffered Feature', type=type('string'))

 def execute(self):
  buffer = ogr.CreateGeometryFromWkt( \
   self.wkt.getValue()).Buffer(self.buffer.getValue())
  self.out.setValue(_genOutputFormat(buffer, self.format.getValue()))
  buffer.Destroy()

 def _setNamespace(xml, prefix, uri):

  return xml.replace('>', ' xmlns:%s="%s">' % (prefix, uri), 1)

 def _genOutputFormat(geom, format):

 if format == 'gml':

  return _setNamespace(geom.ExportToGML(), 'gml', \
     'http://www.opengis.net/gml')

 if format == 'json':

  return geom.ExportToJson()

 if format == 'wkt':

  return geom.ExportToWkt()

Notes:

• _setNamespace is a workaround, as OGR’s ExportToGML doesn’t declare a namespace prefix / uri in the output, which would make the ExecuteResponse XML choke parsers
• _genOutputFormat is a utility method, which can be applied to any OGR geometry object

As you can see, very easy to pull off, integrates and extends easy.  Kudos to the OGR and PyWPS teams!

Great post by Dave here on his experience and suggestions / ideas on finding a job.  Upbeat, positive and encouraging.  Congratulations and good post Dave!

There are several ways to apply for a job, one is through virtual means, but the other where there are more options is the physical means, once you get a job the best thing you can do with that money is login pragmatic play and shoot yourself to heaven with the profits you can make.

I recently had the opportunity to prototype WMS visualization of meteorological data.  MapServer, GDAL and Python to the rescue!  Here are the steps I took to make it happen.

Welcome to the captivating realm of luxury timepieces, where sophistication meets craftsmanship and exclusivity reigns supreme. In this guide, we will take you on an exhilarating journey through the mesmerizing world of Richard Mille replica watches – a parallel universe where impeccable designs and intricate details intertwine with affordability. Brace yourself as we unveil the hidden gems that mimic these iconic timepieces flawlessly, alongside revealing the best-kept secrets on where to find these sought-after fakes. Whether you’re an aficionado looking to expand your collection or simply curious about the artistry behind high-end replicas, prepare to be captivated by a dazzling array of horological wonders that redefine what it truly means to own a prestigious Richard Mille watch.

The data, (GRIB), is a GDAL supported format, so MapServer can handle processing as a result.  The goal here was to create a LAYER object.  First thing was to figure out the projection, then figure out the band pixel values/ranges and correlate to MapServer classes (in this case I just used a simple greyscale approach).

Here’s the hack:

import sys
import osgeo.gdal as gdal
import osgeo.osr as osr

if len(sys.argv) < 3:
 print 'Usage: %s <file> <numclasses>' % sys.argv[0]
 sys.exit(1)

cvr = 256  # range of RGB values
numclasses = int(sys.argv[2])  # number of classifiers

ds = gdal.Open(sys.argv[1])

# get proj4 def and write out PROJECTION object
p = osr.SpatialReference()
s = p.ImportFromWkt(ds.GetProjection())
p2 = p.ExportToProj4().split()

print '  PROJECTION'
for i in p2:
 print '   "%s"' % i.replace('+','')
print '  END'

# get band pixel data ranges and classify
band = ds.GetRasterBand(1)
min = band.GetMinimum()
max = band.GetMaximum()

if min is None or max is None:  # compute automagically
 (min, max) = band.ComputeRasterMinMax(1)

# calculate range of pixel values
pixel_value_range = float(max - min)
# calculate the intervals of values based on classes specified
pixel_interval = pixel_value_range / numclasses
# calculate the intervals of color values
color_interval = (pixel_interval * cvr) / pixel_value_range

for i in range(numclasses):
 print '''  CLASS
  NAME "%.2f to %.2f"
  EXPRESSION ([pixel] >= %.2f AND [pixel] < %.2f)
  STYLE
   COLOR %s %s %s
  END
 END''' % (min, min+pixel_interval, min, min+pixel_interval, cvr, cvr, cvr)
 min += pixel_interval
 cvr -= int(color_interval)

Running this script outputs various bits for MapServer mapfile configuration.  Passing more classes to the script creates more CLASS objects, resulting in a smoother looking image.

Here’s an example GetMap request:

Users can query to obtain pixel values (water temperature in this case) via GetFeatureInfo.  Given that these are produced frequently, we can use the WMS GetMap TIME parameter to create time series maps of the models.

I’ve had some time to work on CSW support in OWSLib in the last few days.  Some thoughts and updates:

FGDC Support Added

Some CSW endpoints out there serve up GetRecords responses in FGDC CSDGM format.  This has now been added to trunk (mandatory elements + eainfo).  Note that csw:Record (DMCI + ows:BoundingBox) and ISO 19139 are already supported.  One tricky bit here is that FGDC was/is mostly implemented without a namespace, which CSW requires as an outputSchema parameter value.  I’ve used http://www.fgdc.gov for now.

Both FGDC and ISO (moreso ISO) have deep and complex content models, so if there are elements that you don’t see supported in OWSLib, please file an feature request ticket in trac, and I’ll make sure to implement (and add to the doctests).

Metadata Identifiers are Important!

When parsing GetRecords requests, we store records in a Python dict, using /gmd:MD_Metadata/gmd:fileIdentifier (for ISO), /csw:Record/dc:identifier (for CSW’s baseline) or /metadata/idinfo/datasetid (for FGDC).  Some responses return metadata without these ids for whatever reason.  This sets the dict key to Python’s None type, which ends up overwriting the dict key’s entry.  Not good.  I implemented a fix to set a random, non-persistent identifier so as not to lose data.

Of course, the best solution here would be for providers to set identifiers accordingly from the start.  Then again, what happens when CSW endpoints harvest other CSWs and identifiers are the same?  Perhaps a namespace of some sort for the CSW would help.  This would be an interesting interoperability experiment.

Harvest Support Added

I implemented a first pass of supporting Harvest operations.  CSW endpoints usually require authentication here, which may vary by the implementation.  Therefore, I’ve left this logic out of OWSLib as it’s not part of the standard per se.

Transaction Support Coming

Sebastian Benthall of OpenGeo has indicated that they are using OWSLib’s CSW support for some of their projects (awesome!), and has kindly submitted a patch for an optional element issue (thanks Sebastian!).  He also indicated that Transaction support would be of interest, so I’ve started to think about this one.  As with the Harvest operation, Transaction support also requires some sort of authentication, which we’ll leave to the client implementation.  Most of the work will be with marshalling the request, as response handling is very similar to Harvest responses.

Give it a go, submit bugs and enhancements to the OWSLib trac.  Enjoy!

I recently had a question on how to do batch centroid calculations against GIS data. OGR to the rescue again!

Richard Mille replica is a luxury watch brand founded in 1999 by French businessman and watchmaker, Richard Mille. Known for its innovative designs and high-tech materials, Richard Mille has become one of the most sought after brands in the world of luxury watches.

However, with prices ranging from tens of thousands to millions of dollars, owning an authentic Richard Mille watch may seem like an unattainable dream for many. This is where the concept of replica watches comes into play.

Replica watches are essentially copies or imitations of high-end designer watches that mimic their appearance and functionality. These replicas offer a more affordable alternative for those who desire the style and prestige associated with luxury brands like Richard Mille.

But not all replica watches are created equal. There are various grades and qualities available in the market, making it crucial to do thorough research before making a purchase. And when it comes to Richard Mille replicas, it is essential to have a good understanding of what sets them apart from other replica watches.

Using OGR’s Python bindings (GDAL/OGR needs to be built –with-geos=yes), one can process, say, an ESRI Shapefile, and calculate a centroid for each feature.

The script below does exactly this, and writes out a new dataset (any input / output format supported by OGR).

import sys
import osgeo.ogr as ogr

# process args
if len(sys.argv) < 4:
 print 'Usage: %s <format> <input> <output>' % sys.argv[0]
 sys.exit(1)

# open input file
dataset_in = ogr.Open(sys.argv[2])
if dataset_in is None:
 print 'Open failed.\n'
 sys.exit(2)

layer_in = dataset_in.GetLayer(0)
feature_in = layer_in.GetNextFeature()

# create output
driver_out = ogr.GetDriverByName(sys.argv[1])
if driver_out is None:
 print '%s driver not available.\n' % sys.argv[1]
 sys.exit(3)

dataset_out = driver_out.CreateDataSource(sys.argv[3])
if dataset_out is None:
 print 'Creation of output file failed.\n'
 sys.exit(4)

layer_out = dataset_out.CreateLayer(sys.argv[3], None, ogr.wkbPoint)
if layer_out is None:
 print 'Layer creation failed.\n'
 sys.exit(5)

# setup attributes
feature_in_defn = layer_in.GetLayerDefn()

for i in range(feature_in_defn.GetFieldCount()):
 field_def = feature_in_defn.GetFieldDefn(i)
 if layer_out.CreateField(field_def) != 0:
  print 'Creating %s field failed.\n' % field_def.GetNameRef()

layer_in.ResetReading()
feature_in = layer_in.GetNextFeature()

# loop over input features, calculate centroid and output features
while feature_in is not None:
 feature_out_defn = layer_out.GetLayerDefn()
 feature_out = ogr.Feature(feature_out_defn)
 for i in range(feature_out_defn.GetFieldCount()):
  feature_out.SetField(feature_out_defn.GetFieldDefn(i).GetNameRef(), \
  feature_in.GetField(i))
  geom = feature_in.GetGeometryRef()
  centroid = geom.Centroid()
  feature_out.SetGeometry(centroid)
  if layer_out.CreateFeature(feature_out) != 0:
   print 'Failed to create feature.\n'
   sys.exit(6)
  feature_in = layer_in.GetNextFeature()

# cleanup
dataset_in.Destroy()
dataset_out.Destroy()

Oh Lord! You cannot imagine how stressful it is to program this type of software. Actually I like it a lot, I don’t deny it, it’s very challenging, but sometimes stress beats me and my head hurts, my stomach hurts, it makes me dizzy, or I even get nervous.

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