How to have continuous features for a shapefile ?
I mean NOT cut by the dateline to respect [-180:180] longitude excursion that I do not
want to respect.
Here is an example where I display the Russia shapefile in a leaflet map.
In fact I would like to have continuous continent.
Shapefile comes from
https://gadm.org/about.html
Any command from gdal or ogr2ogr to merge separated features ?
Thanks
If you load the GADM level-0 layer into QGIS and toggle Show Feature Count, you'll see that, even though the shape seems split, the actual layer only has a single feature:
Your shape gets cut off because the polygon crosses the boundary in the projection you are using and gets wrapped around. This doesn't mean the features get actually split.
If you want to display it as a continuous feature, you need to specify an appropriate projection. For instance, using the example here gives me this:
This is just one way, there might be different projections that fit your purpose better. Also, getting this done in leaflet is a different question.
Related
TL;DR: can anyone help w/ geospatial things in R using geom_raster() in ggplot?
Basically it seems that my issue is stemming from the fact that I don't have perfectly gridded values (aka I get this message: "Warning: Raster pixels are placed at uneven vertical/horizontal intervals and will be shifted. Consider using geom_tile() instead."). So, if I switch to geom_tile, then I can control the size of the tiles, but then they look chunky and awful since there's no interpolation feature in geom_tile. If I use geom_raster, I think it gets confused what size it should plot the pixels since it's not perfectly gridded, so when I facet my ggplot, the output sometimes will have teeny tiny dots (or even no dots at all) on some facets, and pretty maps on the other facets. When I round the lat/lon coordinates to 0 decimal places, this fixes the teeny tiny / no dots problem, but then ends up with things just like geom_tile (chunky with no blending between).Any ideas on how to fix this? I think if there's a way to manually interpolate to fill in NA values so that I do have nice symmetrically-gridded data, then geom_raster should work fine. Like what needs to happen is for each situation where we're missing a value at a certain lat/lon, we need to take the mean value between the two closest neighboring points to fill in that missing lat/lon point on the grid. But I'm not sure how to do this (aka how to convert from my dataframe into all the different spatial classes and back again). Then again, this manual approach might be overcomplicating things and I'd love a simpler solution. (fingers crossed)
I'm building this in a shiny app with crazy long code, and a very large dataset, but happy to share additional info as needed!
example plot
example plot2
warning example
I am looking for a way to implement that QGIS Filter to smooth agriculture zones within a field. Right now I have many artifacts. I want to smooth and eliminate artifacts smaller than specified number of pixels and instead pick neighboring max.
I tried using ndimage.grey_dilation and got an acceptable result, but still have artifacts at then end (that I'd like to remove without re-running through a new dilation).
The mentioned QGIS filter seems to do something better.
Just curious. I know it is called morphological opening, but results are not awesome.
See below:
I am currently writing a code in Python for flows through pipes. In this regard, I have to find a centre line passing thorough a 3D pipe geometry defined by a nastran mesh (cells with three or four edges whose coordinates i can access). I am using the pyNastran module in Python to get all the relevant data and functions.
My question is what would be the most efficient way of finding the centre line of the pipe. The pipe is a 3d pipe with bends in all direction. ( I have all the coordinates of every single point on the mesh in an array)
That is not so easy topic :-/ Problem is that center line is not local property.
For sure, each point on center line corresponds to one slice through pipe or simpler to one perimeter on pipe surface. For any kind of defining that relation on local characteristics, it is easy to find example with bendings or changing pipe diameter that produces 'strange' result.
One way to solve it is to look for topics that have similar properties that are needed here. Like we want slices to be 'parallel' and to uniformly pass through pipe. On one project we used heat diffusion/transfer to tackle a problem. Idea is to put boundary conditions, on one pipe side set boundary condition 1 and on the other side boundary condition 0. Heat will transfer from one side to the other and isoterm will have good properties.
After that, choose center line discterization (on [0-1]), for each point find isoterm on that temperature and find center of mass of that isoterm. Connecting these centers will produce center line.
It is possible to make diffusion on 3D (volume) and 2D (surface) case. It is probably faster to do it on surface.
So upon seeing images from Godus I was wondering how to generate a simple, non-interactive, 2D image with different colors for different heights or layers of heights like on the picture below.
I was just thinking in terms of generating the basic layers of colors for the topography without the houses, trees objects and units. I wasn't thinking in terms of creating a graphics engine that would solve this, but a simple way to generate a flat image on the screen.
The question is two-fold:
1, What kind of data could be used for this sort of generation? I was thinking maybe ASCII art which is kind of easy to create and modify to quickly change the topography, but would be difficult to provide height information.
2, What existing frameworks, classes, methods or methodologies could be used for solving the generation after having the data ready.
Godus:
ASCII art (northern europe with ! for Norway, # for Sweden, $ for Finland and % for Russia:
(Taken from the MapBox docs: http://mapbox.com/developers/utfgrid/#map_data_as_ascii_art)
If you want to create a simple 2D, contoured image, I would try the following:
Create some height data. I'd just use a grey-scale image for that, rather than ascii. You can author basic height-maps in MS Paint, or anything similar.
Smooth the data. For example, apply a blur, or increase the resolution using a smooth filter.
Consider clamping all height data below a certain point - this represents a water level, if you want that.
Quantise the data. The more you quantise, the fewer but more obvious the contours.
Apply a false colouring, via a palette lookup. For example a : low lying areas blue, for water, then yellow, for sand, green for grass, brown for earth, grey for rock, and white for snow.
The important parts are the enlarging/smoothing filter, which creates more interesting shapes to your contours, and the quantisation which actually creates the contours themselves.
You can play with the stages of this. For example you could introduce some noise to the terrain, to make it look more natural if your source data is very clean. Or you could increase the smoothing if you want everything very rounded.
If you want to use ascii, you could just generate a bitmap directly from that, which wouldn't be tricky. The ascii you use as an example though is split up by country rather than terrain, so the false-colouring and contouring would probably do the wrong thing. You could probably use it as input to a simple terrain generator, perhaps just having a couple of chars to denote where you want land, sea, mountains, etc.
Here's a very basic example I knocked up, it's just an application of the technique I suggested. I didn't use any frameworks or libs, just a few simple image processing functions, and an height-map of Europe I found:
I have a seemingly simple problem, but an easy solution is alluding me. I have a very large series (tens or hundreds of thousands of points), and I just need to visualize it at different zoom levels, but generally zoomed well out. Basically, I want to plot it in a tool like Matlab or Pyplot, but knowing that each pixel can't represent the potentially many hundreds of points that map to it, I'd like to see both the min and the max of all the array entries that map to a pixel, so that I can generally understand what's going on. Is there a simple way of doing this?
Try hexbin. By setting the reduce_C_function I think you can get what you want. Ex:
import matplotlib.pyplot as plt
import numpy as np
plt.hexbin(x,y,C=C, reduce_C_function=np.max) # C = f(x,y)
would give you a hexagonal heatmap where the color in the pixel is the maximum value in the bin.
If you only want to bin in one direction, see this this method.
First option you may want to try is Gephi- https://gephi.org/
Here is another option, though I'm not quite sure it will work. It's hard to say without seeing the data.
Try going to this link- http://bl.ocks.org/3887118. Do you see toward the bottom of the page data.tsv with all of the values? IF you can save your data to resemble this then the HTML code above should be able to build your data in the scatter plot example shown in that link.
Otherwise, try visiting this link to fashion your data to a more appropriate web page.
There are a set of research tools called TimeSearcher 1--3 that provide some examples of how to deal with large time-series datasets. Below are some example images from TimeSearcher 2 and 3.
I realized that simple plot() in MATLAB actually gives me more or less what I want. When zoomed out, it renders all of the datapoints that map to a pixel column as vertical line segments from the minimum to the maximum within the set, so as not to obscure the function's actual behavior. I used area() to increase the contrast.