How can I read the .las header file to determine what spatial reference system (i.e projection system, datum, etc) that a LiDAR point cloud is using?
The best answer can be found in the ASPRS specifications for what should be included in an LAS header. Look for the LASF_Projection portion of the file.
The projection information for the point data is required for all
data. The projection information will be placed in the Variable Length
Records. Placing the projection information within the Variable Length
Records allows for any projection to be defined including custom
projections. The GeoTIFF specification is the model for
representing the projection information, and the format is explicitly
defined by this specification.
GeoKeyDirectoryTag Record: (mandatory)
User ID: LASF_Projection
Record ID: 34735
This record contains the key values that define the coordinate system.
The answer for Erica is correct but partially (is based on the 1.2 las version), as in the new version of the .las format (1.4) there are quite important changes on the OORDINATE REFERENCE SYSTEM (CRS) REPRESENTATION:
Take a look at this:
http://www.asprs.org/a/society/committees/standards/LAS_1_4_r13.pdf
Related
I know that there are minimum uniform buffer and shader storage minimum alignments, but I was wondering if there were minimum alignment for vertices(anything that is read from the input assembler) and indices. Also do the staging buffers need to have an alignment for indices and vertices? How about the copy operations from staging buffer to a device-local buffer and vice versa?
vkCmdBindIndexBuffer's documentation states that offset "must be a multiple of the type indicated by indexType".
The vertex buffer binding functions have similar alignment requirements based on the formats used for them, but they are specified in a more unusual way (and not in the documentation for the function).
There is a section in the specification on how the address for a specific attribute is computed. The wording here puts for a set of de-facto requirements on the pOffsets parameter to vkCmdBindVertexBuffers and similar functions.
The rules boil down to this: you have to specify offsets (and other fields) such that the eventual address computed for each attribute is not misaligned, relative to the format for that attribute. Packed formats have to be multiples of their pack size, while non-packed formats have to be multiples of their component sizes. So while VK_FORMAT_A8B8G8R8_UNORM_PACK32 must be aligned to 4 bytes, VK_FORMAT_R8G8B8A8_UNORM can handle byte-alignment.
Though personally, I wouldn't test the latter.
It is explained on https://www.r-spatial.org/r/2017/06/22/spatial-index.html that "The R tree is constructed on the first argument (x), and used to match all geometries on the second argument (y) of binary functions". However, it is finally not explained what are the criteria that could guide the user in the choice of the sf object to put in the first argument. Are there some rules of thumb for this choice?
Generally, with creating spatial indexes you want to start with a larger envelopes that decompose to smaller envelopes. So, if you are extending from that then starting with a geometry with a larger extent then building smaller envelopes from that would match the general schema of how these selections are optimized.
I have a csv files, that it has the following structure.
ERP,J,JACKSON,8388 SOUTH CALIFORNIA ST.,TUCSON,AZ,85708,267-3352,,ALLENTON,MI,48002,810,710-0470,369-98-6555,462-11-4610,1953-05-00,F,
MARKETING,J,JACKSON,8388 SOUTH CALIFORNIA ST.,TUCSON,AZ,85708,267-3352,,ALLENTON,MI,48002,810,710-0470,369-98-6555,462-11-4610,1953-05-00,F,
As you can see there is not header, but for your information the first part (first column) represents the sector where are getting the data.
What I have to do is depending on the first column value, for example (MARKETING or ERP) I have to send all that rows to a different output directory.
For example, all rows with ERP to /output/ERP/
all rows with MARKETING to /output/marketing/
I have an idea about how to do it, but my problem is about the RouteOnAttribute processor I am using, I don't know how to refer to the first column and to indicate what is the value (ERP or MARKETING) to later on send it to the correct output directory.
Here is my schema.
Thanks.
Use PartitionRecord processor for this case.
Configure the processor with record reader/writer controller services. Even though if you are not having header you can use col1,col2...etc in avro schema.
add new property that defines processor to use that field for partition the flowfile.
Now partition record processor adds the partition field attribute with value, by making use of this attribute value we can dynamically store files into respected directories dynamically.
Flow:
1.GetFile
2.PartitionRecord
3.PutFile //configure directory as /output/${<keep_partition_field_name_here>}
Please refer this link for configuring usage of partition record processor.
(or)
Old Approach:
Using RouteText processor instead of SplitText + RouteOnAttribute Processors
Configure RouteText processor as
Use the ERP/MARKETING connections connect to PutFile processor and use RouteText.Route attribute value to dynamically save the files into Directories.
Flow:
1.GetFile
2.RouteText
3.PutFile //configure directory as /output/${RouteText.Route}/
You can also use Group Regular expression property value to create partitions.
Note
Using PartitionRecord processor will be more efficient than RouteText processor.
I'm developing a new webpage in (.NET framework, if that helps) for the below scenario. Every single day, we get a cab drivers report.
Date | Blob
-------------------------------------------------------------
15/07 | {"DriverName1":"100kms", "DriverName2":"10kms", "Hash":"Value"...}
16/07 | {"DriverName1":"50kms", "DriverName3":"100kms", "Hash":"Value"}
Notice that the 'Blob' is the actual data received in json format - contains information about the distance covered by a driver at that particular day.
I have written a service which reads the above table & further breaks down this and puts it into a new table like below:
Date | DriverName | KmsDriven
15/07 DriverName1 100
15/07 DriverName2 10
16/07 DriverName3 100
16/07 DriverName1 50
By populating this, I can easily do the following queries:
How many drivers drove on that particular day.
How is 'DriverName1' did for that particular week, etc.,
My questions here are:
Are there anything in .NET / SQL world to specifically address this or let me know if I am reinventing the wheel here.
Is this the right way to use the Blob data ?
Are there any design patterns to adhere here to ?
Are there anything in .NET / SQL world to specifically address this or
let me know if I am reinventing the wheel here.
Well, there are JSON parsers available, for example Newtonsoft's Json.NET. Or you can use SQL Server's own functions. Once you have extracted individual values from JSON, you can write them into corresponding columns (in your new table).
Is this the right way to use the Blob data?
No. It violates the principle of atomicity, and therefore the first normal form.
Are there any design patterns to adhere here to?
I'm not sure about "patterns", but I don't see why would you need a BLOB in this case.
Assuming the data is uniform (i.e. it always has the same fields), you can just declare the columns you need and write directly to them (as you already proposed).
Otherwise, you may consider using SQL Server's XML data type, which will enable you to extract some of the sections within an XML document, or insert a new section without replacing your whole document.
Does anyone have any experience adding their own data to GapMinder, the really cool software that Hans Rosling uses in his TED talks? I have an array od objects in JSON that would be easy to show in moving bubbles. This would be really cool.
I can see that my Ubuntu box has what looks like data in /opt/Gapminder Desktop/share/assets/graphs/world, but I would need to figure out:
How to add a measure to a graph
How to add a data series
How to set the time range of the data
Identify the measures to follow at each time step
and so on.
Just for the record: if you want to use Gapminder with your own dataset, you have to convert your data in a format suitable to Gapminder. More specifically, looking in the assets/graphs/world, you will have to:
Edit the file overview.xml, which contains the tree structure of all the indicators (just copy/paste an entry and specify your own data);
Convert your data copying the structure of the xml files in that directory (this is the tricky part): you can specify some metadata in the preamble, and then specify your own data series, with something like:
<t1 m="i20,50.0,99.0,1992" d="90.0, ... ,50.0, ..."/> where i20 is the country id, which is followed by the minima and maxima of the series, and the year it refers to.
In my humble opinion, Gapminder is a great app but it definitely needs more work on integration with other datasets. Way better to use Google Motion Chart as you did, or MooGraph (site and doc), which is unfortunately not as great as Gapminder.
#Stefano
the information you provided is very valuable. Is somewhere available a detailed specification of the XML files containing the data?
Anyway, just to enrich your response, I also found that:
overview.xml file
The link between Nations and their IDs is in this file
The structure of the menus for the selection of the indicators is also in the same file (at the bottom) under the section <indicatorCategorization>
The structure of the datafile XML
For each line the year represents the first year of the serie, and then the values follow one per year, comma separated.
Grazie,
Max
I ended up using the google motion chart API. I ended up with this.