Ral To Ansi Conversion
Genciana Haggins
I've asked this before in a round-about manner before here on Stack Overflow, and want to get it right this time. How do I convert ANSI (Codepage 1252) to UTF-8, while preserving the special characters? (I am aware that UTF-8 supports a larger character set than ANSI, but it is okay if I can preserve all UTF-8 characters that are supported by ANSI and substitute the rest with a ? or something)
I am basically writing a program that splits vCard files (VCF) into individual files, each containing a single contact. I've noticed that Nokia and Sony Ericsson phones save the backup VCF file in UTF-8 (without BOM), but Android saves it in ANSI (1252). And God knows in what formats the other phones save them in!
If you need to detect the encoding, you can read the file as bytes and then look for character codes that are specific for either encoding. If the file contains no special characters, either encoding will work as the characters 32..127 are the same for both encodings.
VCF is encoded in utf-8 as demanded by the spec in chapter 3.4. You need to take this seriously, the format would be utterly useless if that wasn't cast in stone. If you are seeing some Android app mangling accented characters then work from the assumption that this is a bug in that app. Or more likely, that it got bad info from somewhere else. Your attempt to correct the encoding would then cause more problems because your version of the card will never match the original.
You convert from 1252 to utf-8 with Encoding.GetEncoding(1252).GetString(), passing in a byte[]. Do not ever try to write code that reads a string and whacks it into a byte[] so you can use the conversion method, that just makes the encoding problems a lot worse. In other words, you'd need to read the file with FileStream, not StreamReader. But again, avoid fixing other people's problems.
I found this question while working to process a large collection of ancient text files into well formatted PDFs. None of the files have a BOM, and the oldest of the files contain Codepage 1252 code points that cause incorrect decoding to UTF8. This happens only some of the time, UTF8 works the majority of the time. Also, the latest of the text data DOES contain UTF8 code points, so it's a mixed bag.
So, I also set out "to detect which encoding the input file has" and after reading How to detect the character encoding of a text file? and How to determine the encoding of text? arrived at the conclusion that this would be difficult at best.
So, I solved my problem with the following code. Since only a small amount of my text data contains difficult character code points, I don't mind the performance overhead of the exception handling, especially since this only had to run once. Perhaps there are more clever ways of avoiding the try/catch but I did not bother with devising one.
It's also worth noting that the StreamReader class has constructors that take a specific Encoding object, and as I have shown you can adjust the EncoderFallback/DecoderFallback behavior to suit your needs. So if you need a StreamReader or StreamWriter for finer grained work, this approach can still be used.
How I solved this:I have vCard file (*.vcf) - 200 contacts in one file in russian language...I opened it with vCardOrganizer 2.1 program then made Split to divide it on 200....and what I see - contacts with messy symbols, only thing I can read it numbers :-) ...
Steps: (when you do this steps be patient, sometimes it takes time)Open vCard file (my file size was 3mb) with "notepad"Then go from Menu - File-Save As..in opened window choose file name, dont forget put .vcf , and encoding - ANSI or UTF-8...and finally click Save..I converted filename.vcf (UTF-8) to filename.vcf (ANSI) - nothing lost and perfect readable russian language...if you have quest write: [email protected]
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Customers purchasing their first projector generally seek to acquire the brightest projector within their spending range. However, there is an inconsistency within the market on how brightness is measured, so how do we make sense of the various brightness specifications? This issue is a result of the fact that some brands on the market choose not to use the internationally recognized brightness standard adopted by the majority of brands, ANSI brightness, but instead advertise brightness specs in different ways. The most common are: ANSI brightness, LED brightness, and light source brightness.
While each of these three methods uses the word "lumens" to describe a given level of brightness, each method measures and defines a "lumen" quite differently. The differences in their definitions cause their values to fluctuate wildly. This can lead to cases where a projector might list a brightness value of 1,000 ANSI lumens, while an equivalent competing model might list their lumens as 2,400 because it uses an LED light source. The question then is: if all of these types of brightness are measured in lumens, why are their values so drastically different?
*In general, a portion of the brands that use LED brightness publicize an increase over ANSI brightness by a factor of 2.4 for LED brightness, but these numbers are speculations based on the most ideal lighting conditions (such as in a dark room).
*The light source brightness for a normal projector with the best optics and light conversion (the amount of light available after processing by the color wheel, mirrors, and lenses) at most is roughly 16 times the ANSI brightness.
The American National Standards Institute (ANSI) is a private, non-profit organization that administers and coordinates the U.S. voluntary standards and conformity assessment system. Founded in 1918, the Institute works in close collaboration with stakeholders from industry and government to identify and develop standards, and conformance, based solutions to national and global priorities.
Two systems of weights and measures are derived from English Units: the US Customary System and the British Imperial System. Neither is dominant worldwide, but they do retain significant usage. For commercial and everyday use, the US Customary System is used in the United States, and the Imperial System still applies to volume and vehicle speed in the United Kingdom.
Therefore, it is important to understand US Customary and Imperial units and their conversions to metric. In fact, there have been some substantial errors in the past associated with unit-based misunderstandings. Most notably, in 1999, due to a mishap with one engineering team using English units while another used SI units, a $125 million Mars orbiter was lost to the great expanse of space.
When spark.sql.storeAssignmentPolicy is set to ANSI, Spark SQL complies with the ANSI store assignment rules. This is a separate configuration because its default value is ANSI, while the configuration spark.sql.ansi.enabled is disabled by default.
The following subsections present behaviour changes in arithmetic operations, type conversions, and SQL parsing when the ANSI mode enabled. For type conversions in Spark SQL, there are three kinds of them and this article will introduce them one by one: cast, store assignment and type coercion.
In Spark SQL, arithmetic operations performed on numeric types (with the exception of decimal) are not checked for overflows by default.This means that in case an operation causes overflows, the result is the same with the corresponding operation in a Java/Scala program (e.g., if the sum of 2 integers is higher than the maximum value representable, the result is a negative number).On the other hand, Spark SQL returns null for decimal overflows.When spark.sql.ansi.enabled is set to true and an overflow occurs in numeric and interval arithmetic operations, it throws an arithmetic exception at runtime.
As mentioned at the beginning, when spark.sql.storeAssignmentPolicy is set to ANSI(which is the default value), Spark SQL complies with the ANSI store assignment rules on table insertions. The valid combinations of source and target data type in table insertions are given by the following table.
When spark.sql.ansi.enabled is set to true, Spark SQL uses several rules that govern how conflicts between data types are resolved.At the heart of this conflict resolution is the Type Precedence List which defines whether values of a given data type can be promoted to another data type implicitly.
A household-scale system with solid and liquid disinfection in the backend. In the frontend, feces and urine/wash water are mechanically separated by a specifically designed mechanism that can be attached to standard squat plates. Attachment to pedestals is also possible. Dewatered fecal material is then mixed with granular particles and smoldered. Catalytic conversion of the generated is used to dry incoming fecal material in situ, and thermally disinfected liquid waste.
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