Latest Contributions by DanPatterson

Time for an update... The original... Table tools for Pro... circa 2020  The toolbox locations Tools_for_ArcGIS_Pro  and inside there. Table Tools Updates are related to changes in the esri arcgis pro ecosystem and enhancements and changes in NumPy. Lots of tools to choose from Enjoy ... View more

Read the help topics... 1 Featureclass To NumPy Array  FeatureClassToNumPyArray can be used to pull the vertices out of geometry features. If they are singlepart without holes, then you have an exact representation that can be used to recreate the inputs. If that isn't the case then reconstruction takes some work (see https://github.com/Dan-Patterson/numpy_geometry for the `npg` module). 2 NumPy Array to Featureclass  NumPyArrayToFeatureClass will not overwrite an existing feature class, even if the overwriteOutput environment is set to True. ------------------------------ Stepwise... a notebook is attached # ---- Imports import arcpy import numpy as np from numpy.lib.recfunctions import structured_to_unstructured as stu from numpy.lib.recfunctions import append_fields # like arcpy ExtendTable I will assume that you are using a notebook in ArcGIS Pro.  I will demo a simple conversion of polygons to all points.  The other options can also be coded for, or simply buy a higher license level of Pro.   Assume that your layer is in a map.  Specify the layer name and whether you want to carry over  extra table fields. # Specify the layer to use for the `lyr_name` variable # This only works for featureclasses... no further error checking... the pressure is on # lyr_name = 'sq3' # -- input the featureclass name extra_fields = True # -- True keeps all extra fields, False otherwise... just some? write your own   Now the big step. check that the layer exists in the active map.  If it does, carry on, otherwise bail. the layer datasource and the spatial reference is obtained, a list of fields to dump from the output (lines 11-15) an array is created (lines 16-21).  This is a numpy array produced the arcpy.da function designed for this purpose. some cleanup follows, output fields are specified and appended to the output (if any) the output featureclass is created (line 36) and added to the map (line 37) # ---- Project, map and layer collection # The current project with the active map is checked for the layer name you specified above. # If it exists, the vertices will be converted to a point featureclass. # aprx = arcpy.mp.ArcGISProject("CURRENT") map_ = aprx.activeMap lyrs = map_.listLayers(lyr_name) if lyrs: in_fc = lyrs[0].dataSource # -- path to the featureclass SR = arcpy.Describe(in_fc).spatialReference ignore_flds = ['OBJECTID', 'Shape', 'Shape_Area', 'Shape_Length'] # -- add if you want keep_flds = ["OID@", "SHAPE@X", "SHAPE@Y"] if extra_fields: add_flds = [f.name for f in arcpy.ListFields(in_fc) if f.name not in ignore_flds] keep_flds = keep_flds + add_flds arr = arcpy.da.FeatureClassToNumPyArray( in_fc, field_names=keep_flds, spatial_reference=SR, explode_to_points=True ) # -- output featureclass checks out_fc = in_fc + "_pnts" if arcpy.Exists(out_fc): # -- delete if it exists arcpy.management.Delete(out_fc) # # -- array checks and combining coordinate values dt_ = arr.dtype dt_names = [(i[0].replace("@", "_"), i[1]) for i in arr.dtype.descr] arr.dtype = dt_names # final_arr = append_fields(arr, ["Pnt_x", "Pnt_y"], [arr["SHAPE_X"], arr["SHAPE_Y"]]) print("\nOutput fields :\n{}".format(final_arr.dtype.names)) # # -- produce the output and add to the map. arcpy.da.NumPyArrayToFeatureClass(final_arr, out_fc, ["SHAPE_X", "SHAPE_Y"], spatial_reference=SR) map_.addDataFromPath(out_fc) # -- add results to the map # else: print("No layer called {}".format(lyr_name)) And the results are I will spare you the table contents, but they are there. -------------- If you prefer the python window import arcpy import numpy as np in_fc = r"C:\...path to your layer...\...layer_name..." keep_flds = ["OID@", "SHAPE@X", "SHAPE@Y"] SR = arcpy.Describe(in_fc).spatialReference arr = arcpy.da.FeatureClassToNumPyArray( in_fc, field_names=keep_flds, spatial_reference=SR, explode_to_points=True ) Which shows the structured array data format. (snipped to simplify) arr array([(1, 300009. , 5000001. ), (1, 300000. , 5000000. ), (1, 300002. , 5000008. ), (1, 300008. , 5000010. ), ... snip (7, 300004. , 5000009. ), (7, 300002. , 5000009. ), (7, 300002. , 5000011. )], dtype=[('OID@', '<i4'), ('SHAPE@X', '<f8'), ('SHAPE@Y', '<f8')]) You can then split the array into its constituent shapes using the OID@ field.  Basically determine the difference in the sequential id values, where they differ from 0, get the positional index to use for splitting. spl = np.nonzero(np.diff(arr["OID@"]))[0] + 1 From there, you can produce subarrays subs = np.array_split(arr, spl) subs[0] # -- the first as an example array([(1, 300009.000, 5000001.000), (1, 300000.000, 5000000.000), (1, 300002.000, 5000008.000), (1, 300008.000, 5000010.000), (1, 300010.000, 5000010.000), (1, 300010.000, 5000008.000), (1, 300009.000, 5000001.000), (1, 300003.000, 5000003.000), (1, 300007.000, 5000003.000), (1, 300005.000, 5000007.000), (1, 300003.000, 5000003.000)], dtype=[('OID@', '<i4'), ('SHAPE@X', '<f8'), ('SHAPE@Y', '<f8')]) From there you could process to just take the first or last point (one of the main tool options that requires and advanced licence). frst = [i[0] for i in subs] # let's grab the first point of each shape frst [(1, 300009.000, 5000001.000), (2, 300010.000, 5000008.000), (3, 300008.000, 5000011.000), (4, 300006.000, 5000012.000), (5, 300002.500, 5000013.000), (6, 300004.000, 5000012.500), (7, 300002.000, 5000011.000)] As a teaser, you can even place a point at a certain percentage along the polygon perimeter with a little numpy magic from numpy.lib.recfunctions import structured_to_unstructured as stu base = [stu(a[["SHAPE@X", "SHAPE@Y"]]) for a in subs] halfway = [_percent_along_(a, percent=50) for a in base] halfway [array([ 300010.000, 5000009.950]), array([ 300008.500, 5000012.000]), array([ 300005.000, 5000010.310]), array([ 300005.056, 5000014.972]), array([ 300000.214, 5000010.643]), array([ 300002.527, 5000012.991]), array([ 300003.894, 5000009.000])] and the function for placing the points def _percent_along_(a, percent=0): """Add a point along a poly feature at a distance from the start point.""" # a = _base_(a) if percent > 1.: percent /= 100. dxdy = a[1:, :] - a[:-1, :] # coordinate differences leng = np.sqrt(np.einsum('ij,ij->i', dxdy, dxdy)) # segment lengths cumleng = np.concatenate(([0], np.cumsum(leng))) perleng = cumleng / cumleng[-1] if percent <= 0: # check for faulty distance or start point return a[0] if percent >= perleng[-1]: # check for greater distance than cumulative return a[-1] _end_ = np.digitize(percent, perleng) x1, y1 = a[_end_] _start_ = _end_ - 1 x0, y0 = a[_start_] t = percent - perleng[_start_] xt = x0 * (1. - t) + (x1 * t) yt = y0 * (1. - t) + (y1 * t) return np.array([xt, yt]) Or you can 'arcpy' it if you prefer to crank up Pro. So, basic functionality shouldn't be completely removed by license levels.  If you have the need convert features to points, remember that tools are available within.     ... View more