I have loaded a raster using stars package and then created tiles over the raster. Now i subset the raster based on these tile.
tiles[[i]] <- st_bbox(c(xmin=x0,ymin=y0,xmax=x1,ymax=y1),crs=st_crs(r))
crop_tiles[[i]] <- r[tiles[[i]]]
Here r is raster loaded using read_stars("filename.tif").
Now i want to remove among the crop_tiles the empty ones. i.e the tiles where the raster values are NA
You can check if all values of a stars object named r are NA with:
all(is.na(r[[1]]))
then remove those tiles where the above is equal to TRUE.
For more specific code, please provide reproducible sample data in your question, thanks.
Related
I am not sure if this is possible with folium. I want to take the polygons value of the city of Toronto and map it over an area of Glimakra, Sweden. This is for an article I am writing about living in the countryside is not much different than living in a big city.
I want to take this polygon border of Toronto
and place it over the Glimakra, Sweden area.
I wrote this code and because my geojson data file for Toronto placed Toronto where it should be from a GEO perspective.
import folium
m = folium.Map(
location=[56.30507180,14.13632150],
zoom_start=11
)
folium.GeoJson("toronto_geojson.geojson", name="geojson").add_to(m)
folium.LayerControl().add_to(m)
m.save("index.html")
m
My question is, how can I turn my toronto_geojson.geojson into a file with the polygon shape without the geojson stuff so that I can place the shape over any map area. Similar to how I can use the folium plugin HeatMap to place a heatmap over a map area.
Does anyone know the best practice to do this?
from folium.plugins import HeatMap
import os
m = folium.Map([56.30507180,14.13632150], tiles='Stamen Toner', zoom_start=6)
HeatMap(data).add_to(m)
m.save(os.path.join('heatmap.html'))
m
I have an unstructured grid vtk file that contains three different types of cells (Tetrahedral, Wedge and Hexahedral). This file contains multiple Scalars (8 attributes such as Pressure, Temperature e.t.c.) and a Single Vector (U,V,W) and I am trying to create a surface plot from this file for a Scalar or Vector at a time using the Vedo python wrapper for vtk. The vtk file contains a scalar or vector value for each cell, including the point coordinates.
I have read the documentation over and over, with examples here https://vtkplotter.embl.es/content/vtkplotter/index.html. These are the things that I have tried with the challenge that I am having with each method:
Method 1: Loading the file as a TetMesh
vp = Plotter()
test = load('Case_60.vtk')
vp.show(test)
This method doesn't plot Scalar Values and only shows points. No Solid Surface. Tried using a cuttertool() with it , it throws an error saying non-Tetrahedral Cell Encountered.
Method 2: Using the UGrid
ug = UGrid('Case_60.vtk')
show(ug)
This method plots as surface with a solid color. Does not seem to be picking the Scalars.
What is the proper way for me to display surface plot and display the scalar value for each cell? Is Vedo able to do what I'm trying to do?
You might need to specify which array is to be used for coloring, e.g.:
from vedo import *
ug = UGrid(datadir+'limb_ugrid.vtk')
print(ug.getArrayNames())
ug.selectCellArray('chem_0')
show(ug, axes=True)
if this doesn't work for your mesh please submit an issue here.
I have a hard time, figuring out a proper affine transformation for 3 different views i.e. coronal, axial and saggital, each having separate issues like below:
1: Axial color map get overlapped with the saggital original view.
2: Similarly Sagittal color map gets overlapped with the axial original image.
3: And everyone has some kind of orientation issues like best visible here when the color map and original image for coronal come correct but with wrong orientation.
I am saving the original file that I am sending to the server for some kind of prediction, which generates a color map and returns that file for visualization, later I am displaying everything together.
In server after prediction, here is the code to save the file.
nifti_img = nib.MGHImage(idx, affine, header=header)
Whereas affine and header are the original affine and header extracted from the file I sent.
I need to process "idx" value that holds the raw data in Numpy array format, but not sure what exactly to be done. Need help here.
Was trying hard to solve the issue using nibabel python library, but due to very limited knowledge of mine about how these files work and about affine transformation, I am having a hard time figuring out what should I do to make them correct.
I am using AMI js with threejs support in the frontend and nibabel with python in the back end. Solution on the frontend or back end anywhere is acceptable.
Please help. Thanks in advance.
img = nib.load(img_path)
# check the orientation you wanna reorient.
# For example, the original orientation of img is RPI,
# you wanna reorient it to RAS, the second the third axes should be flipped
# ornt[P, 1] is flip of axis N, where 1 means no flip and -1 means flip.
ornt = np.array([[0, 1],
[1, -1],
[2, -1]])
img_orient = img.as_reoriented(ornt)
nib.save(img_orient, img_path)
It was simple, using numpy.moveaxis() and numpy.flip() operation on rawdata from nibabel. as below.
# Getting raw data back to process for better orienation and label mapping.
orig_img_data = nib.MGHImage(numpy_arr, affine)
nifti_img = nib.MGHImage(segmented_arr_output, affine)
# Getting original and predicted data to preprocess to original shape and view for visualisation.
orig_img = orig_img_data.get_fdata()
seg_img = nifti_img.get_fdata()
# Placing proper views in proper place and flipping it for a better visualisation as required.
# moveaxis to get original order.
orig_img_ = np.moveaxis(orig_img, -1, 0)
seg_img = np.moveaxis(seg_img, -1, 0)
# Flip axis to overcome mirror image/ flipped view.
orig_img_ = np.flip(orig_img_, 2)
seg_img = np.flip(seg_img, 2)
orig_img_data_ = nib.MGHImage(orig_img_.astype(np.uint8), np.eye(4), header)
nifti_img_ = nib.MGHImage(seg_img.astype(np.uint8), np.eye(4), header)
Note: It's very important to have same affine matrix to wrap both of these array back. A 4*4 Identity matrix is better rather than using original affine matrix as that was creating problem for me.
I am using the spatial plug-ins for TOS to perform the following task:
I have a dataset with X and Y coordinates. I have also a shapefile with multi polygons and two metadata attributes, name and Id. The idea is to look-up the names in the shapefile with the coordinates. With a point in polygon will be determined which polygon belongs a point to.
I am using the shapefile input component which points to the .shp file.
I am facing to hurdles:
I cannot retrieve the name and Id from the file. I can only see an attribute call the_geom. How can I read the metadata?
The second thing is, the file contains a multi polygon and I don't know how to iterate over it in order to perform a Contains or intersect with the points.
Any comment will be highly appreciated.
thanks for your input #chrki
I managed to solve my tasks in this way:
1) Create a generic schema under metadata:
As the .dbf file was in the same directory of the shapefile Talend automatically recognized the metadata:
2) This is the job overview:
3) I read the shape file using a sShapeFileInput component:
4) The shapefile contains multipolygons and I want to have polygons. My solution was to use a sSimplify component. I used the default settings.
5) The projection of the shapefile was "MGI / Austria Lambert" which corresponds to EPSG 31287. I want to re-project it as EPSG 4326 (GCS_WGS_1984) which is the one used by my input coordinates.
6) I read the x, y coordinates from a csv file.
7) With a s2DPointReplacer I converted the x,y coordinates as Point(x,y) (WKT)
8) Finally I created an expression in a tMap to get only the polygons and points with an intersection. I guess a "contains" would also work:
I hope this helps someone else.
Kind regards,
Paul
I'm using GraphViz to determine the controls location in my C# application.
But i'm not being able to give the graphViz dot generator the width of the output.
This are the paremeters of the dot file that im using
Digraph x {
autosize=false;
size="25.7,8.3!";
resolution=100;
node [shape=rect];
(edges definitions comes here)
...
But seems to have no effect on the generated plaintext file.
Am I missing something do set the page width?
Regards
I added a -> b to your example. Here's the plaintext output I get:
digraph x {
graph [autosize=false, size="25.7,8.3!", resolution=100];
node [label="\N", shape=rect];
graph [bb="0,0,54,108"];
a [pos="27,90", width=0.75, height=0.5];
b [pos="27,18", width=0.75, height=0.5];
a -> b [pos="e,27,36.104 27,71.697 27,63.983 27,54.712 27,46.112"];
}
As you can see, the size and resolution attributes are included in the output.
You may change the values of size and resolution, this won't change anything else than those attributes in the plaintext output. The positions of all nodes and edges are relative to the bounding box (bb) of the graph.
However, if you decide for example to output a png, graphviz will use this information to scale the bounding box according to your size and resolution attributes and calculate the final image size.
In this example, the resulting png will be 444 by 831 pixels (8.3 inches with a resolution of 100 dpi result in 830 pixels, the pixel on top is probably due to a rounding error).
You may find more detailed examples about size attribute and the resulting image size in this answer.