I don't believe you can access that text because you are using a Single Symbol. If you read the help file on layer objects in the arcpy mapping module a property of a layer is symbologyType which will return OTHER for single symbol and that is not a supported type. If you were using Unique Value, a supported type, then you would be able to access those text ...
If you always want the part before first underscore then split at this and fetch first item (index 0) in split list. Set will remove duplicates.
arcpy.env.workspace = r'C:\Default.gdb'
polygons = list(set([fc.split('_') for fc in arcpy.ListFeatureClasses(feature_type='POLYGON')]))
You can also use re module to split at first non-letter ...
Use the polyline to raster tool and set the slope raster as the extent and snap raster in the tool environment settings. Use a Con statement in the raster calculator tool:
Con(IsNull("yourlineraster"), "yoursloperaster", 0)
The usability of the arcpy walk function could definitely be tweaked. It is efficient for listing, but that's it. Any filters massively hamstring it
Ok so I've found something that works for me.
Incredible that I missed the Tabulate Area tool in the ArcGIS Spatial Analyst.
It does what I need, and incredibly fast.
Using the 30m raster as a data source and the fishnet grid as the zones (with a unique ID field), I can get the fractional coverage of each land-cover type in each grid cell. It really ...
First, confirm the coordinate system of your data by right clicking on the layer and going to Source. In this case, it shows as the following
Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_Sphere
Projection: Mercator_Auxiliary_Sphere False_Easting: 0.00000000
False_Northing: 0.00000000 Central_Meridian: 0.00000000
If I understand correctly, the top symbol is the rotation you are looking to match on the remaining symbols. If using ArcMap you can go into the symbol properties, click on the symbol within the table of contents, and enter the desired angle.
If you are going for a desired affect, it helps if you know the angle of the line. If you aren't sure, and using ...
You have 11 points and a very flat variogram. This means there appears to be no spatial structure and your data is random noise. Any best prediction at a non-sample location is going to be the sample average.
You could modify the variogram binning or variogram parameters and get something with a bit of spatial structure, but that might be more luck than ...
Here is the solution:
Replace polygons by points using "Feature to point"
"Extract multi values to points" chose as raster the cut&fill. This will add the FID of the raster to the points.
Join by attributes the cut&fill with the last points
Edit to remove unnecessary columns and join to the polygon
a possible solution to get the statistics correctly after clipping the image is to save the statistics of the original image as XML and load the saved XML file to the clippied image.
To do that:
Go to the Properties of the original image -> Symbology -> Under Statistics, click on Save as XML
Load the same XML file to the original image from the Properties ...
The Table Compare tool should do the trick:
I can't see any specific info about reading databases, but a quick test had no issues reading a GeoDB so it should be fine once the data is in the Arc environment.
This second link is the archived help for version 10.1 ...
When no other information is available, the most robust method would be to take the average precipitation inside your polygons. This can be done with the Spatial join tool of ArcGIS with the JOIN_ONE_TO_ONE option. If some polygons do not include any points, you can also use the "CLOSEST" matching option.
I'm not sure why GEE adds a black border to exported images, I think it's something to do with the re-orienting the image to the new projection system and cannot be changed. This isn't an issue if you're exporting a satellite image raster, as it sets the border pixel values to class 0 and this doesn't interfere with the pixels in the rest of the image.
From what I understand, you want to create individual shape files of each area. Each shape file contains the points that fall within the geographic borders of that area.
If so, do a ‘spatial join’ first. This will create a separate shapefile of the points with an attribute in which area it is located in. Your points are the target_feature.
Afterwards you ...
If you define your tool parameters as layers (feature and raster layers) the tool will not accept a path to a feature class or raster since they're not layers. Feature/raster layers and table views are in-memory representations of your data. Feature layers might be accepted for a parameter of type 'feature class', but the opposite is not true.
So, change ...
It appears that these comments by @MichaelStimson answered your question:
Have you set your Cell Size
and Snap Raster
to the raster being extracted from before the extraction? ...
I was inspired by @FelixIP, but I wanted to write a solution without joins or the creation of extra files, since my network is quite large with 400K+ pipes and 500K+ nodes.
The geometric network build forces the X,Y of the nodes and the pipe ends to be coincident. You can access these locations with the shape tokens in arcpy cursors and match them. The ...
I have tested solution from answer above and on my real world data the difference is negligible. Opposite to results in other answer, my times for arcpy.MakeTableView_management and arcpy.da.SearchCursor within ArcMap are same same.
I have tested variations with and without query, please see the code for query version, and final measured results below: