Latest Contributions by VinceE

Appreciate the input, thanks! This option will have to do for now. Here's what my implementation looks like: HHMM is a TEXT field that the user types in, HH is a FLOAT field that gets calculated. Figured reading HH would be more intuitive to understand than MM, but the concept is the same. I try to use the same "template" for Attribute Rules, so this is probably overly verbose, but here's the automatic calculation from HH:MM text to HH float:   /* Converts a text HH:MM format field and converts it to a float HH field. */ //---------------------------------------------------------------------// // FUNCTIONS function calculateHH(hhmm) { // Set up results dict. If all checks pass, return this as-is. var result = Dictionary("VALUE", null, "ERROR", null) if (IsEmpty(hhmm)) { return result } Console(hhmm) var timeArr = Split(hhmm, ":") var hh = Number(timeArr[0]) + (Number(timeArr[1]) / 60) Console(`${hhmm} --> ${hh}`) result["VALUE"] = Round(hh, 2) return result } //---------------------------------------------------------------------// // INPUTS var hhmm = $feature.Hiking_HHMM //---------------------------------------------------------------------// // MAIN var fldUpdates = Dictionary() // Convert HHMM text to MM float ---------------- var result = calculateHH(hhmm) if (!IsEmpty(result["ERROR"])) {return {"errorMessage": `ERROR: ${result}`}} else {fldUpdates["Hiking_HH"] = result["VALUE"]} Console({"result": {"attributes": fldUpdates}}) return {"result": {"attributes": fldUpdates}}     And my solution to verifying the initial HH:MM inputs:   /* Script provides validation for a TEXT field that should contain a time duration in the format H?H?:MM. There are several checks, since RegEx is not available. 1) Blank strings are not allowed; null values are fine 2) Format must contain a colon--as in 3:30, 12:04, :37 3) Values on either side of the colon must be numeric 4) Hours are optional, but must be 0 - 23 5) Minutes must always be two characters, and numeric values 0 - 59 This script is meant to be used as Calculation type AR, rather than a Constaint AR. */ //---------------------------------------------------------------------// // FUNCTIONS function timeValidate (time) { // Set up results dict. If all checks pass, return this as-is. var result = Dictionary("VALUE", time, "ERROR", null) // First check empty, then null. Can't do both simultaneously nicely. if ((TypeOf(time) == "String") && Trim(time) == "") { result["ERROR"] = "Blank input is not allowed." return result } else if (IsEmpty(time)) { return result } // Console(timeArray, Count(timeArray)); // Must contain one ":", yields two elements with Split(). if (Count(timeArray) != 2) { result["ERROR"] = "Format must contain one colon." return result } // Check if both values in the array are numbers. for (var i in timeArray) { var tPart = timeArray[i] Console(`PART: ${i}, VAL: ${tPart}, ASNUM: ${Number(tPart)}, ISNAN: ${IsNan(Number(tPart))}`) // If Number returns NaN, "Casting a non-numeric text or undefined to a number". if (IsNan(Number(tPart))) { result["ERROR"] = "At least one value is not numeric." return result } // Hours validation, the first array item. if (i == 0) { Console(`VALIDATING HOURS: ${tPart}`) // Comparison operators coerce Text to Number for comparison. if (tPart > 23 || tPart < 0) { result["ERROR"] = "Hours must be 0-23." return result } } // Minutes validation, the second array item. else if (i == 1) { Console(`VALIDATING MINUTES: ${tPart}`) if (Count(tPart) != 2) { result["ERROR"] = "Minutes must be two digits." return result } else if (tPart > 59 || tPart < 0) { result["ERROR"] = "Minutes must be 0-59." return result } } } return result } //---------------------------------------------------------------------// // INPUTS // Expects($feature, "Shape_Length", "MPH", "Miles") var time = $feature.Hiking_HHMM // DERIVED var timeArray = Split(time, ":") Console(`TIME ARR: ${timeArray}`) //---------------------------------------------------------------------// // MAIN var fldUpdates = Dictionary() // result = {"VALUE": time, "ERROR": error string OR null} var result = timeValidate(time) // If an error is returned in results dict, show and make no changes. if (!IsEmpty(result["ERROR"])) {return {"errorMessage": `ERROR: ${result} Please use H?H?:MM format.`}} // Else, add the field update information to the running updates log. else {fldUpdates["Hiking_HHMM"] = time} Console({"result": {"attributes": fldUpdates}}) return {"result": {"attributes": fldUpdates}}     ... View more

For "data" reasons, a great solution--at the resolution I require, even minutes would work. My issue is that if a user is transcribing a record from elsewhere, say "7:37", they have to do the math to convert those hours into minutes (or hours and minutes into seconds, if seconds are my unit). Not a huge deal, but an extra step, and possibly error-prone. The other thing is quickly reading the table. I want to be able to read these records at a glance. Number of seconds (beyond a minute or two) means virtually nothing to me, and even minutes can get difficult: 407 minutes takes me a little while to process. So I'd really want that to show as HH:MM. And I don't know of a table "display" setting that would allow the math required to calculate that from seconds/minutes alone, and an Attribute Rule would... change the actual underlying data value? Plus, an Attribute Rule returning a value with the colon would be problematic with a DOUBLE field type. Storing as minutes is probably going to be the only solution here, but it's annoying that it can't be displayed in an easily readable way. My best bet might be to use an Attribute Rule to calculate the minutes into a SHORT/DOUBLE field from a TEXT field. So the user can type in 5:49 or whatever in the TEXT field, and then an Attribute Rule calculates the raw number of minutes into a different numeric field, and then valid sorting can take place on the MINUTES field. Appreciate the comment, thanks! ... View more

Your point about data types (String vs. NoneType) is an interesting one I hadn't considered, thanks for that. I can't replicate Pro (3.X) throwing an error regarding "nulling" a non-nullable field though, unless you're talking about using Field Calculator to "<Null>" a field, in which case it does yield an error. Thanks again for the detailed response. ... View more

I think I understand what you're after. I reworked some of the code internals to simplify things, so I'd try copying this version, rather than updating anything you're currently using (unless you're comfortable doing that). The outputs should be the same, with the addition of the new label field. I'm using a label expression to show the "point_id" + "quadrant" labels. This takes the angle of the line (say, 45 degrees) and then assigns the quadrant (NE). It then inverts that to get the opposing quadrant (SW). The code is set up so that if the line is perfectly north-south or east-west (90 degrees, 180 degrees) then those labels will be "N" and "S" or "W" and "E", without the secondary direction. Let me know if something doesn't work! Here's an W-E example, which I suppose would only occur... ~1% of the time?     """ RANDOM ANGLE TRANSECT LINES CENTERED ON SAMPLING POINTS """ #-----------------------------------------------------------------------------# # IMPORTS import math import os import random import sys from typing import Literal, Union, Tuple import arcpy #-----------------------------------------------------------------------------# # INPUTS # Full path to the geodatabase where the sampling points are, and where outputs will go. workspace = r"your_full_path\geodatabase_name.gdb" # Name of the point feature class in the above geodatabase that houses the # sampling points. in_pnt_fc = "_SAMPLE_POINT" # Distance between sampling points (along x and y, not diagonals). # Units match coordinate system. point_spacing = 400 # feet, in my case box_width = 80 # NORTH_BOX: squares will be oriented to grid-north # NORTH_DIAMOND: diamonds will be oriented to grid-north # ANGLE_BOX: square sides oriented to the tranect lines # ANGLE_DIAMOND: diamonds angled to the transect lines box_rotation = 'ANGLE_BOX' # OPTIONS: (NORTH_BOX, NORTH_DIAMOND, ANGLE_BOX, ANGLE_DIAMOND) # Name of the output feature class to be created. fc_line = "TRANSECT_LINE" fc_endpoints = "TRANSECT_LINE_ENDPOINTS" fc_box = f"TRANSECT_{box_rotation}" # -----------------------------------------------------------------------------# # SETTINGS # This just overwrites previous outputs to help with testing. arcpy.env.overwriteOutput = True #-----------------------------------------------------------------------------# # FUNCTIONS def rotate_point(coordinate: tuple[int, int], pivot_point: tuple[int, int], angle: Union[int, float], angle_format: Literal['DEGREES', 'RADIANS']='DEGREES' ) -> tuple[float, float]: """Rotates coordinate values around another point. :coordinate: (x, y) coordinate pair to be rotated :pivot_point: (x, y) coordinate pair around which point will be rotated :angle: angle to rotate the coordinate :angle_format: angle format, RADIANS or DEGREES |(x_prime, y_prime)| rotated point coordinates""" if angle_format == 'DEGREES': angle = math.radians(angle) x, y = coordinate X, Y = pivot_point x_trans = x - X y_trans = y - Y sin = math.sin(angle) cos = math.cos(angle) # Counter-clockwise rotation: x_transprime = cos * x_trans - sin * y_trans y_transprime = sin * x_trans + cos * y_trans x_prime = x_transprime + X y_prime = y_transprime + Y # print(f"XY TRANS: {x_trans:,.0f}, {y_trans:,.0f}\n" # f"XY TRANSPRIME: {x_transprime:,.0f}, {y_transprime:,.0f}\n" # f"XY PRIME: {x_prime:,.0f}, {y_prime:,.0f}\n") return (x_prime, y_prime) def create_box(method: Literal['NORTH_BOX', 'NORTH_DIAMOND', 'ANGLE_BOX', 'ANGLE_DIAMOND'], center_pnt: arcpy.Point, width: Union[int, float], angle: Union[int, float], spat_ref: int) -> arcpy.Polygon: """Create a square box around a given point. Optionally rotate it. :center_pnt: centroid of the square box :width: width of side of box :angle: angle the box should be rotated |box_geom| geometry object representing the box""" angle_dict = {'NORTH_BOX': 45, 'NORTH_DIAMOND': 0, 'ANGLE_BOX': angle + 45, 'ANGLE_DIAMOND': angle} # Distance to move point based on known hypotenuse (1/2 width of box). offset = math.sqrt(2 * ((width / 2) ** 2)) x, y = center_pnt.X, center_pnt.Y Nx, Ny = x, y + offset Ex, Ey = x + offset, y Sx, Sy = x, y - offset Wx, Wy = x - offset, y box_corners = [] for coordinate in [(Nx, Ny), (Ex, Ey), (Sx, Sy), (Wx, Wy), (Nx, Ny)]: box_corners.append(rotate_point(coordinate=coordinate, pivot_point=(x, y), angle=angle_dict[method], angle_format='DEGREES')) pnt_array = arcpy.Array([arcpy.Point(*corner) for corner in box_corners]) box_geom = arcpy.Polygon(pnt_array, spatial_reference=spat_ref) return box_geom def get_angle_quadrant(a): """As previously defined, angle resctricted [1-360].""" # Check for 90 degree cardinal directions. if a == 90: return "N" elif a == 180: return "W" elif a == 270: return "S" elif a == 360: return "E" # Determine non-90 quadrants. if 0 < a < 90: return "NE" elif 90 < a < 180: return "NW" elif 180 < a < 270: return "SW" elif 270 < a < 360: return "SE" else: raise ValueError("Angle value must be numeric and between 1-360, inclusive.") #-----------------------------------------------------------------------------# # MAIN in_points = os.path.join(workspace, in_pnt_fc) transect_lines = [] sample_box_dict = {} pnt_desc = arcpy.da.Describe(in_points) pnt_oid_fld = pnt_desc['OIDFieldName'] pnt_spat_ref = pnt_desc['spatialReference'].factoryCode with arcpy.da.SearchCursor(in_points, [pnt_oid_fld, 'SHAPE@XY']) as scurs: for oid, (x, y) in scurs: # Random angle in degrees, 1-360; convert to radians. angle_deg = random.randint(1, 360) angle_rad = math.radians(angle_deg) print(f"POINT {oid}, ANGLE: {angle_deg}") # Get x/y offset of random point on the imaginary circle around each point. start_x = (point_spacing/2) * math.cos(angle_rad) start_y = (point_spacing/2) * math.sin(angle_rad) # Using the sample point coordinate as the starting point, # calculate real-world x and y of both start and end of transect line. start_point = arcpy.Point(x + start_x, y + start_y) end_point = arcpy.Point(x - start_x, y - start_y) # -S/-E suffixes differentiate start/end boxes. Not currently used, but # documented just in case (also, unique keys are required). sample_box_dict[f'{oid}-S'] = create_box(box_rotation, start_point, box_width, angle_deg, pnt_spat_ref) sample_box_dict[f'{oid}-E'] = create_box(box_rotation, end_point, box_width, angle_deg, pnt_spat_ref) # Create a Polyline Geometry Object. Append dict entry to list. new_line = arcpy.Polyline(arcpy.Array([start_point, end_point])) transect_lines.append({'OID': oid, 'GEOM': new_line, 'ANGLE': angle_deg}) # Create three feature classes. for fc, geom in [(fc_line, 'POLYLINE'), (fc_endpoints, 'POINT'), (fc_box, 'POLYGON')]: arcpy.management.CreateFeatureclass(out_path=workspace, out_name=fc, geometry_type=geom, spatial_reference=pnt_spat_ref) print(f"CREATED FC {fc}") # FC paths. ln_fc = os.path.join(workspace, fc_line) pt_fc = os.path.join(workspace, fc_endpoints) box_fc = os.path.join(workspace, fc_box) # Add and ID field to the points, the lines, and box FCs. for fc in (ln_fc, pt_fc, box_fc): arcpy.management.AddField(fc, 'POINT_OID', 'SHORT') # Add angle field to the line FC. arcpy.management.AddField(ln_fc, 'LINE_ANGLE', 'SHORT') # Add the point type and the point angle to the point FC. arcpy.management.AddField(pt_fc, 'POINT_TYPE', 'TEXT', field_length=5) arcpy.management.AddField(pt_fc, 'POINT_QUAD', 'TEXT', field_length=5) print("ALL FIELDS ADDED") # Write transect line features----------------------------------# with arcpy.da.InsertCursor(ln_fc, ['SHAPE@', 'POINT_OID', 'LINE_ANGLE']) as icurs: for feature_dict in transect_lines: icurs.insertRow([feature_dict['GEOM'], feature_dict['OID'], feature_dict['ANGLE']]) print("TRANSECTS WRITTEN") # Write endpoint features----------------------------------# invert_quad = {"NE": "SW", "SE": "NW", "SW": "NE", "NW": "SE", "N": "S", "S": "N", "E": "W", "W": "E", } with arcpy.da.InsertCursor(pt_fc, ['SHAPE@', 'POINT_OID', 'POINT_TYPE', 'POINT_QUAD']) as icurs: for feature_dict in transect_lines: start_pnt = feature_dict['GEOM'].firstPoint end_pnt = feature_dict['GEOM'].lastPoint angle = feature_dict['ANGLE'] quadrant_1 = get_angle_quadrant(angle) quadrant_2 = invert_quad[quadrant_1] icurs.insertRow([start_pnt, feature_dict['OID'], "START", quadrant_1]) icurs.insertRow([end_pnt, feature_dict['OID'], "END", quadrant_2]) print("ENDPOINTS WRITTEN") # Create Sampling Zones----------------------------------# # Transect Line Oriented with arcpy.da.InsertCursor(box_fc, ['SHAPE@', 'POINT_OID']) as icurs: for oid, geom in sample_box_dict.items(): icurs.insertRow([geom, oid.split('-')[0]]) print("BOXES WRITTEN")     ... 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I tested several methods of adding fields to a blank feature class in an attempt to overcome the sluggish process that is "AddField" in a for loop. Posting all the code might make this unreadable, so here is a summary. All tests result in the same output**--field names are the same, domains are the same, etc. In this example, I am adding 500 fields to a newly created feature class. There is no data involved here, this is  schema setup only. Methods & Times: Add fields in a for loop to a feature class already saved to file – 6:19 Add fields in a for loop to a memory feature class, then use CreateFeatureclass with the memory copy as the template – 0:56 Using AddFields (batch version with the “s”) – 4:56 FieldMappings() to create fields, ExportFeatures to file, AssignDomainToField in a loop – 2:09 FieldMappings() to create fields, ExportFeatures to memory, AssignDomainToField, CreateFeatureclass to file using memory as template – 0:19 So, in my experiments anyway, using a FieldMappings object and working in memory for as long as possible is the absolute fastest. BUT, simply adding fields to a memory feature class and using that as a template during the export to file is probably the simplest solution in terms of lines of code and maintenance.  Results from a more reasonable 40 fields, in the same order as above: 0:25 0:02 0:14 0:09 0:02 So even when generating a handful of feature classes with am more modest number of fields, it might best to avoid using AddField in a for loop for a feature class that is already saved to file. **AddFields does not allow the user to specify Scale and Precision, and I didn’t see a quick way to modify those attributes after the fact. In a FGDB, it is irrelevant, but this might matter in an enterprise environment. Here's an example of the randomly generated fields for three of the five output methods (all three should be the same).   ... View more

I suppose it is basically the same thing as manipulating fields as one normally would using FieldMappings in FeatureClassToFeatureClass (arcpy or the GUI in Pro)--but in this case, there aren't any fields to start with, just a blank feature class in memory. A bit unintuitive, but it's fast to process! Especially if you don't need to worry about defaults or domains, apparently. As you said, I'll have to just implement the standard AssignDomainToField() and AssignDefaultToField() geoprocessing tools after the initial FieldMapping setup. It's frustrating to not be able to access those properties of the Field object in a writeable way during setup though. Thanks again for all the input! ... View more

That's correct, that is what I'm doing. The Field Mappings object also contains the names of the fields, the lengths of the fields, the types of the fields, etc. (in this example, it's just one field, but it could be more). I am not changing any fields (they don't exist yet), I am creating a new FieldMappings object to be used during the creation of a new Feature Class. Those other properties are correctly transferred from the FieldMappings object to the new Feature Class with FeatureClassToFeatureClass and the field_mappings= parameter. Why wouldn't a writeable "domain" attribute function the same way? I am explicitly trying to get away from AddField (the GP tool you have linked). As I stated, it is much slower than using field maps as above. For example, when adding the same 100 fields using two different methods: A for loop using AddField takes 334 seconds Using field mappings in the FeatureClassToFeatureClass parameter followed by AssignDomainToField takes 109 seconds. 104 of those seconds is the time spent looping over AssignDomainToField. AddFields (with the "s") was mentioned below by @DavidSolari, which was helpful, but doesn't work for me without access to Precision, Scale, and Nullability. So, ideally, I would be able to get the writeable ".domain" attribute to work, along with the "defaultValue" property. Maybe I need to modify these during a second pass over the feature class? As it is, I'm getting that exception listed above thrown when trying to simply access "defaultValue". I presume it's usable without error somehow... At this point, it has become a curiosity thing, more than an optimization thing. I appreciate your time, and thanks for the input! ... View more

I'll certainly take a look, but that's essentially what I'm doing with the FieldMappings--creating a template set of fields in memory, and then using FeatureClassToFeatureClass's field_mappings as the template. If I was constantly generating the same outputs over and over, using the template parameter in CreateFeatureclass would indeed be a good solution, but this is sort of for one-off scenarios (in which case I shouldn't get bogged down in over-optimization anyway...)  Again, thanks for the input! ... View more

"AddFields" has its place, however the inability to set Precision, Scale, and Nullability is a deal breaker for my purposes. If iterating back over the fields to change those values is possible, then that could work. In looking at "AlterField," it would appear that only Nullability can be modified using that tool, not Scale or Precision. So then I'm back to FieldMappings. I appreciate the input anyway. ... View more

Thanks for the assistance @Anonymous User, I was able to get this working. I have a follow up question that I'm hoping you may have some insight into. My modified code is below. The extent string is computed beforehand, but seems to work as expected, based on the polyline geometry being read via a SearchCursor. def save_raster(name, output_directory, ext_string): """""" url = r"https://elevation.nationalmap.gov/arcgis/rest/services/3DEPElevation/ImageServer/exportImage?" params = {"f": "image", "bbox": ext_string, "bboxSR": 102005, "imageSR": 102005, "format": "tiff", "pixelType": "F32", "size": "600,600", "noDataInterpretation": "esriNoDataMatchAny", "interpolation": "RSP_BilinearInterpolation"} response = requests.get(url=url, params=params, allow_redirects=True) name = "".join([char if char in ascii_letters else "" for char in name]) fullpath = pathlib.Path(output_directory, f"{name}.tif") with open(fullpath, mode="wb") as image_file: image_file.write(response.content) arcpy.management.CalculateStatistics(str(fullpath)) The question I have is about specifying pixel size, which oddly enough does not seem to be an option based on the documentation here: https://developers.arcgis.com/rest/services-reference/enterprise/export-image.htm Below my pixels are roughly 10x10 (meters) because I've specified a size of 600,600. Here my pixels are much, much larger because the size I've specified is 30x30, width/height. Using the dimensions of the polyline extent (extent.XMax - extent.XMin) and the intended pixel resulution (30 meters, for example), some math can be performed to get the "size=<width>,<height>" that yields approximately the intended pixel resolution, but it's pretty cumbersome compared to just passing the resolution as a parameter. It's very odd to me that the call takes a pixel count (when is this property relevant?) instead of the actual pixel resolution, which is one of the more important properties of a raster dataset...   ... View more

I'm not totally sure I follow what you're after, but here's an attempt based on my understanding. I have some points, and polygons both with fields "FIELD1" and "FIELD2". For visual purposes, FIELD1 are colors, and FIELD2s are some numeric value, that as per your instructions (I think) are only filled out in the polygon layer. OIDs are shown below for additional clarity, the white lettering with black halos being the polygon layer. My approach is perform a Spatial Join, and then perform comparisons between the FIELD1s--if there is a spatial match, and FIELD1 also matches, then FIELD2 should be copied from the polygon to the point. I don't know what your experience with Python is, but you should be able to update the values in the "INPUTS" section, and either run this externally in the IDE of your choice, or simply paste the below directly into the Python window in ArcGIS Pro and hit "enter." import arcpy arcpy.env.overwriteOutput = True # INPUTS----------------------------------------------------------------------# # All of these "INPUTS" must be updated by the user. # Leave the leading "r" and the quotations. input_point = r"THIS IS A FILE PATH TO YOUR POINT FEATURE CLASS" input_polygon = r"THIS IS A FILE PATH TO YOUR POLYGON FEATURE CLASS" point_flds = ["FIELD1", "FIELD2"] polygon_flds = ["FIELD1", "FIELD2"] output_point = r"THIS IS A FILE PATH TO YOUR OUTPUT POINTS" # MAIN----------------------------------------------------------------------# # This block appends "_1" if input fields are the same between feature classes. # Spatial join output cannot have fields of the same name. updated_polygon_flds = [] for pnt_fld, poly_fld in zip(point_flds, polygon_flds): if pnt_fld == poly_fld: updated_polygon_flds.append(f"{poly_fld}_1") else: updated_polygon_flds.append(poly_fld) # Join the polygon info to the point geometry. Output will be points. arcpy.analysis.SpatialJoin( target_features=input_point, join_features=input_polygon, out_feature_class=output_point, join_operation="JOIN_ONE_TO_MANY", join_type="KEEP_ALL", match_option="INTERSECT", ) # Read the output feature class; specifically, field lists from both inputs. with arcpy.da.UpdateCursor(output_point, point_flds + updated_polygon_flds) as ucurs: for pnt_f1, pnt_f2, poly_f1, poly_f2 in ucurs: # If spatially related, and Field1's are equiavalent, copy over Field2 # from the polygon feature class into the point feature class. if pnt_f1 == poly_f1: ucurs.updateRow([pnt_f1, poly_f2, poly_f1, poly_f2]) Here's the output. To be clear, the first FIELD1 and FIELD2 are from the point, and the second FIELD1 and FIELD 2 are from the polygons. These are aliases of course. For example, the original point #1 (TARGET_FID = 1 in the output) is RED. It's within a RED polygon, therefore we should calculate FIELD2 in the points to be 3, because that's FIELD2 in the polygon. OID 3 in the below table has a "JOIN_FID" of -1, signifying it's not within a polygon at all. So the point-version of FIELD2 is not calculated, and both FIELD1 and FIELD2 (at the end of the table, from the polygon, are null). TARGET_FID 4 is within a polygon, but the colors don't match--FIELD2 not calculated. TARGET_FID 5 is represented twice, because it overlaps two polygons. One of the copies of this point matches the underlying polygon, and is calculated. The other doesn't, and is not calculated. ... View more