Latest Contributions by JohannesLindner

// summary FeatureSets var no_loss_type_1 = {"fields":[{"name":"Category","type":"esriFieldTypeString"},{"name":"FeatureCount","type":"esriFieldTypeInteger"}],"geometryType":"","features":[{"attributes":{"Category":"A - No Impact","FeatureCount":35}},{"attributes":{"Category":"B - Material Excavation","FeatureCount":2}},{"attributes":{"Category":"D - Habitat Restoration","FeatureCount":3}},{"attributes":{"Category":"E - Stormwater Excavation","FeatureCount":8}},{"attributes":{"Category":"F - Utilities","FeatureCount":2}},{"attributes":{"Category":"H - Temporary Impact","FeatureCount":14}}]} var no_loss_type_2 = {"fields":[{"name":"Category","type":"esriFieldTypeString"},{"name":"FeatureCount","type":"esriFieldTypeInteger"}],"geometryType":"","features":[{"attributes":{"Category":"B - Material Excavation","FeatureCount":1}},{"attributes":{"Category":"E - Stormwater Excavation","FeatureCount":3}},{"attributes":{"Category":"F - Utilities","FeatureCount":1}},{"attributes":{"Category":"H - Temporary Impact","FeatureCount":2}}]} no_loss_type_1 = FeatureSet(Text(no_loss_type_1)) no_loss_type_2 = FeatureSet(Text(no_loss_type_2)) // This is ugly, but it works var count_dict = {} // dict of {Category: FeatureCount} var cat_array = [] // to keep track of the dict keys var fs_array = [no_loss_type_1, no_loss_type_2] // FeatureSets to combine for(var i in fs_array) { for(var f in fs_array[i]) { // get category and feature count var cat = f.Category var cnt = f.FeatureCount if(Includes(cat_array, cat)) { // cat already in dict? -> add the dict's value to cnt cnt += count_dict[cat] } else { // new cat? -> append cat to cat_array Push(cat_array, cat) } // add/update the count in count_dict count_dict[cat] = cnt } } // Create output FeatureSet var out_fs = { "fields":[ {"name":"Category","type":"esriFieldTypeString"}, {"name":"FeatureCount","type":"esriFieldTypeInteger"}, ], "geometryType":"", "features":[] } // add features for(var i in cat_array) { var cat = cat_array[i] var f = {"attributes": {"Category": cat, "FeatureCount": count_dict[cat]}} Push(out_fs.features, f) } return FeatureSet(Text(out_fs))   ... View more

A few general remarks: You don't need to call perc_cover_values[sample_id]​, you already have that defined as sample_list You can use a where clause in SearchCursor. No need to select in the layer first. It's better form to let calc_veg_dom only handle the calculation and do the table manipulation in main. This way, all the input and output is in one place instead of hidden in another method. You don't need the sample points at all for this. You know which perc_cover values are from the same sampling site by looking at herb_stratum_repeat.parentglobalid. This also eliminates the need for CalculateField and you can use the UpdateCursor.     import arcpy # GLOBAL VARIABLES ID_FIELD = "globalid" POINT_ID_FIELD = 'parentglobalid' PERC_COVER_FIELD = 'abs_perc_cover_herb' DOM_SPECIES_FIELD = 'dom_species_herb' def calc_veg_dom(sample_dict): """Applies the 50/20 rule for calculating vegetation dominance. The function creates a list that identifies the percent values that are for dominant species for a specific sampling point. Arguments: sample_dict: Dictionary, {ID_FIELD: PERC_COVER_FIELD} Retuns: Dictionary, {ID_FIELD: DOM_SPECIES_FIELD} """ # convert to [ [ID_FIELD, PERC_COVER_FIELD] ], so we can sort sample_list = [[s_id, perc] for s_id, perc in sample_dict.items()] # sort descending by PERC_COVER_FIELD sample_list.sort(key=lambda s: s[1], reverse=True) # calculate the total cover and target cover values total_cover = sum([s[1] for s in sample_list]) perc_cover_50 = total_cover * 0.5 perc_cover_20 = total_cover * 0.2 dom_dict = dict() # 50% rule: species that contribute to more than 50% are dominant, species with tied cover values must be taken together # 20% rule: species with more than 20% coverage are dominant percentages = [] # to check for tied cover values and to calculate cover sum for item in sample_list: if sum(percentages) <= perc_cover_50: percentages.append(item[1]) dom_dict[item[0]] = "Y" elif item[1] in percentages or item[1] >= perc_cover_20: dom_dict[item[0]] = "Y" else: dom_dict[item[0]] = "N" return dom_dict ########################################################################################################### def main(): # You only need the stratum table! fc = arcpy.GetParameterAsText(0) stratum_table = arcpy.GetParameterAsText(1) # load the table data fields = [ID_FIELD, POINT_ID_FIELD, PERC_COVER_FIELD] stratum_data = [row for row in arcpy.da.SearchCursor(stratum_table, fields)] # get uniqe point_ids point_ids = {sd[1] for sd in stratum_data} # set eliminates duplicates # output dict {ID_FIELD: DOM_SPECIES_FIELD} dom_dict = dict() # for each point, get the perc_covers and calculate dominance for point_id in point_ids: # input for calc_veg_dom: {ID_FIELD: PERC_COVER_FIELD} point_stratum_data = {sd[0]: sd[2] for sd in stratum_data if sd[1] == point_id} # output from calc_veg_dom: {ID_FIELD: DOM_SPECIES_FIELD} point_dom_data = calc_veg_dom(point_stratum_data) # add results to dom_dict dom_dict.update(point_dom_data) # write the results into stratum_table fields = [ID_FIELD, DOM_SPECIES_FIELD] with arcpy.da.UpdateCursor(stratum_table, fields) as cursor: for s_id, dom in cursor: try: dom = dom_dict[s_id] cursor.updateRow([s_id, dom]) except KeyError: print("No dominance value found for {} = {}".format(ID_FIELD, s_id)) if __name__ == "__main__": #main() # test calc_veg_dom import random sample_dict = dict() x = 0 while sum(sample_dict.values()) < 100: sample_dict["id_{}".format(x)] = random.randint(5, 40) x += 1 print("in: ", sample_dict) print("out: ", calc_veg_dom(sample_dict))   ... View more