I'm trying to use NaturalEarth cultural vector data to create a topoJSON file with two layers:
US states
World countries
I make the countries topojson file out of the Natural Earth subunits map, omitting the USA, then I use the Natural Earth states map to make a file that contains US states and the USA itself in a layer called "countries". Now, I have two topojson files, each with a layer called "countries". How can I merge these two files into a single one that has a "countries" layer containing the contents of both of their "countries" layers?
Just calling topojson on the files combines them but overwrites one instance of "countries" with another.
I've currently just been using the topojson CLI, but have no problem switching to using the server API if I need to.
Edit: I've resolved the issue by manually concatenating the relevant objects inside topojson files, but am still interested in knowing if there is a solution in the topojson module that I've overlooked.
Related
I am trying to create a topojson file projected using geoAlbersUsa, originating from the US Census's ZCTA (Zip Codes, essentially) shapefile. I was able to successfully get through the examples in the excellent https://medium.com/#mbostock/command-line-cartography-part-1-897aa8f8ca2c using the specified maps, and now I'm trying to get the same result using the Zip Code-level shapefiles.
I keep running into various issues due to the size of the file and the length of the strings within the file. While I have been able to create a geojson file and a topojson file, I haven't been able to give it the geoAlbersUsa projection I want. I was hoping to find something to convert the current topojson file into a topojson file with a geoAlbersUsa projection but I haven't been able to find any way.
I know this can be done programmatically in the browser, but everything I've read indicates that performance will be significantly better if as much as possible can be done in the files themselves first.
Attempt 1: I was able to convert the ZCTA-level shapefile to a geojson file successfully using shp2json (as in Mike Bostock's example) but when I try to run geoproject (from d3-geo-projection) I get errors related to excessive string length. In node (using npm) I installed d3-geo-projection (npm install -g d3-geo-projection) then ran the following:
geoproject "d3.geoAlbersUsa()" < us_zips.geojson > us_zips_albersUsa.json
I get errors stating "Error: Cannot create a string longer than 0x3fffffe7 characters"
Attempt 2: I used ogr2ogr (https://gdal.org/programs/ogr2ogr.html) to create the geojson file (instead of shp2json), then ran tried to run the same geoproject code as above and got the same error.
Attempt 3: I used ogr2ogr to create the geojson sequence file (instead of a geojson file), then ran geo2topo to create the topojson file from the geojsons file. While this succeeded in creating the topojson file, it still doesn't include the geoAlbersUsa projection in the resulting topojson file.
I get from the rather obtuse documentation of ogr2ogr that an output projection can be specified using -a_srs but I can't for the life of me figure out how to specify something that would get me the geoAlbersUsa projection. I found this reference https://spatialreference.org/ref/sr-org/44/ but I think that would get me the Albers and it may chop off Alaska and Hawaii, which is not what I want.
Any suggestions here? I was hoping I'd find a way to change the projection in the topojson file itself since that would avoid the excessively-long-string issue I seem to run into whenever I try to do anything in node that requires the use of the geojson file. It seems like possibly that was something that could be done in earlier versions of topojson (see Ways to project topojson?) but I don't see any way to do it now.
Not quite an answer, but more than a comment..
So, I Googled just "0x3fffffe7" and found this comment on a random Github/NodeJS project, and based on reading it, my gut feeling is that the node stuff, and/or the D3 stuff you're using is reducing your entire ZCTA-level shapefile down to ....a single string stored in memory!! That's not good for a continent-scale map with such granular detail.
Moreover, the person who left that comment suggested that the OP in that case would need a different approach to introduce their dataset to the client. (Which I suppose is a browser?) In your case, might it work if you query out each state's collection of zips into a single shapefile (ogr2ogr can do this using OGR-SQL), which would give you 5 different shapefiles. Then for each of these, run them through your conversions to get json/geoalbers. To test this concept, try exporting just one state and see if everything else works as expected.
That being said, I'm concerned that your approach to this project has an unworkable UI/architectural expectation: I just don't think you can put that much geodata in a browser DIV! How big is the DIV, full screen I hope?!?
My advice would be to think of a different way to present the data. For example an inset-DIV to "select your state", then clicking the state zooms the main DIV to a larger view of that state and simultaneously pulls down and randers that state's-specific ZCTA-level data using the 50 files you prepped using the strategy I mentioned above. Does that make sense?
Here's a quick example for how I expect you can apply the OGR_SQL to your scenario, adapt to fit:
ogr2ogr idaho_zcta.shp USA_zcta.shp -sql "SELECT * FROM USA_zcta WHERE STATE_NAME = 'ID'"
Parameters as follows:
idaho_zcta.shp < this is your new file
USA_zcta.shp < this is your source shapefile
-sql < this signals the OGR_SQL query expression
As for the query itself, a couple tips. First, wrap the whole query string in double-quotes. If something weird happens, try adding leading and trailing spaces to the start and end of your query, like..
" SELECT ... 'ID' "
It's odd I know, but I once had a situation where it only worked that way.
Second, relative to the query, the table name is the same as the shapefile name, only without the ".shp" file extension. I can't remember whether or not there is case-sensitivity between the shapefile name and the query string's table name. If you run into a problem, give the shapefile and all lowercase name and use lowercase in the SQL, too.
As for your projection conversion--you're on your own there. That geoAlbersUSA looks like it's not an industry standard (i.e EPSG-coded) and is D3-specific, intended exclusively for a browser. So ogr2ogr isn't going to handle it. But I agree with the strategy of converting the data in advance. Hopefully the conversion pipeline you already researched will work if you just have much smaller (i.e. state-scale) datasets to put through it.
Good luck.
I trained my model in Nvidia Digits 5 and I would now like to extract the accuracy and loss plots that were generated during training for a report. Is this data saved somewhere so that it would possible to extract the data for these plots so that I could plot it in Python and perhaps ultimately modify the plots to compare different models etc?
The best solution I have found is to either look at the HTML file or to scan the text file caffe_output.log that is produced by Caffe. The text file is usually stored in /var/digits/jobs/insert_your_job_id/ but you can also just run on linux systems:
locate caffe_output.log
Go to your DIGITS job folder and locate your job's subfolder. Inside you'll find a file status.pickle, which is a pickled object containing all your job's information.
You can load it in python like so:
import digits
import pickle
data = pickle.load(open('status.pickle','rb'))
This object is somewhat generic and may contain multiple tasks. For a typical classification task it will likely be just one, but you will still need to access it via data.tasks[0]. From there you can grab the plots:
data.tasks[0].combined_graph_data()
which returns a somewhat convoluted dict (unfortunately - since your network can produce many accuracy/loss outputs, as well as even custom ones). It contains everything you need though - I managed to plot accuracy with:
plt.plot( data.tasks[0].combined_graph_data()['columns'][2][1:] )
but it's likely that you'll have to write a bit of custom code. As always, dir() is your friend.
I am putting together a project where I need to be able to source outside data as a means of inputting skeleton joint positions into Maya. Basically I have a spreadsheet of sequential joint positions for the skeleton which I would like to load into Maya and then link to the skin. Does anyone know a way to upload or reference these positions (as FK into Maya)?
Probably the easiest thing to do is to convert your spreadsheet data to atom format
Atom is a json based format for exchanging animation data and, since its JSON based you should be able to concoct a CSV to ATOM translator using Python's built in csv and json modules.
I'm new at this an essentially have very little idea of what I'm doing.
(FYI I'm working off of this tutorial:
http://bost.ocks.org/mike/map/)
I'm trying to get topojson to work.
I've successfully installed homebrew and node.
I've done the
"npm install -g topojson" part as well.
And then, after that, when I try to type in the "which ogr2ogr" etc -- just, nothing happens.
He says if having trouble to edit path variable environments. I have only a vague idea of what that means, and not sure if that's my problem or not.
Let me know what other information you need. I really just want to make a map. The global install does seem to have worked. I just don't know what to do from here.
The tutorial you linked to is a great starting point. I wish I'd seen it before trying to figure everything out on my own. :)
From what I understand, you probably missed the step in which you install gdal. If you're seeing some other errors, please post them in your question.
You can get ogr2ogr working by running:
brew install gdal
Here's some background info for you, so you'll get a better understanding of what's going on there.
topojson and ogr2ogr are two distinct utilities. ogr2ogr is part of the gdal package and in our case is used to generate GeoJSON from a shapefile.
GDAL is a translator library for raster geospatial data formats that
is released under an X/MIT style Open Source license by the Open
Source Geospatial Foundation. As a library, it presents a single
abstract data model to the calling application for all supported
formats. It also comes with a variety of useful commandline utilities
for data translation and processing.
TopoJSON is used to compress the rather large GeoJSON output from the previous GDAL conversion. It reduces redundancy by specifying paths with arcs rather than discrete points. It's pretty neat, actually:
TopoJSON is an extension of GeoJSON that encodes topology. Rather than
representing geometries discretely, geometries in TopoJSON files are
stitched together from shared line segments called arcs. TopoJSON
eliminates redundancy, offering much more compact representations of
geometry than with GeoJSON; typical TopoJSON files are 80% smaller
than their GeoJSON equivalents. In addition, TopoJSON facilitates
applications that use topology, such as topology-preserving shape
simplification, automatic map coloring, and cartograms.
The output of these two steps (shapefile -> GeoJSON -> TopoJSON) will be a JSON string which is easily interpreted by JavaScript. You'll need to use topojson in your drawing code to convert back to GeoJSON for actually drawing the map.
Recall from earlier the two closely-related JSON geographic data
formats: GeoJSON and TopoJSON. While our data can be stored more
efficiently in TopoJSON, we must convert back to GeoJSON for display.
Breaking this step out to make it explicit:
var subunits = topojson.object(uk, uk.objects.subunits);
For ubuntu, I used this way to have ogr2ogr
sudo apt-get install gdal-bin
In my web application I am going to use OpenLayers.Strategy.AnimatedCluster strategy due to the fact that I need to visualize a great amount of point features. Here is a very good example of what it looks like. In both examples in above mentioned example the data (point features) are generated of taken from the GeoJSON file.
So, can anybody provide me with a file containing 100 000+ (better is even 500 000+) features (world cities, for instance), or explain how I can generate them so that they will be located all over the world (not like in Spain in the first example in above mentioned link).
use a geolocation database to supply you the data you need. GeoLite, for example
If 400K+ locations is ok, use download their CSV CITY LIST
If you want more, then you might want to give the Nominatim downloads, but they are quite bulky (more than 25GB) and parsing data is not as simple as a csv file.