largest_separation = 0
sum_separation = 0
sum_separation_count = 0
for code in merged_codes:
for code_ref in code.all_refs:
diff = position_difference(code.position, code_ref.position)
sum_separation += diff
sum_separation_count += 1
if diff > largest_separation:
largest_separation = diff
average_separation = 0
if sum_separation_count > 0:
average_separation = sum_separation / sum_separation_count
print "From average position largest separation is {} and average is {} meters".format(largest_separation, average_separation)
if not os.path.exists(out_directory):
os.makedirs(out_directory)
# Write everything out to CSV file.
all_results_filename = time.strftime("_results_all-%Y%m%d-%H%M%S.csv")
all_results_filepath = os.path.join(out_directory, all_results_filename)
export_results(all_codes, [], all_results_filepath)
print 'Output all codes to {}'.format(all_results_filepath)
# Write averaged results out to file.
avg_results_filename = time.strftime("_results_averaged-%Y%m%d-%H%M%S.csv")
avg_results_filepath = os.path.join(out_directory, avg_results_filename)
export_results(merged_codes, [], avg_results_filepath)
print 'Output merged codes to {}'.format(avg_results_filepath)
sys.exit(ExitReason.bad_arguments)
else:
survey_items = parse_survey_file(survey_filepath)
if convert_coords:
print "Converting coordinates"
east_offset, north_offset = calculate_east_north_offsets(items, survey_items)
convert_coordinates(items, east_offset, north_offset)
# Now that items are in same coordinates run accuracy checks
run_survey_verification(items, survey_items)
# Write everything out to CSV file to be imported into database.
all_results_filename = time.strftime('results_all-%Y%m%d-%H%M%S.csv')
all_results_filepath = os.path.join(out_directory, all_results_filename)
all_output_items = [ref for item in items for ref in item.all_refs]
export_results(all_output_items, rows, all_results_filepath)
print "Exported all results to " + all_results_filepath
# Output all averaged results to one file.
avg_results_filename = time.strftime("results_averaged-%Y%m%d-%H%M%S.csv")
avg_results_filepath = os.path.join(out_directory, avg_results_filename)
print 'Output averaged {} items'.format(len(items))
export_results(items, rows, avg_results_filepath)
print "Exported averaged results to " + avg_results_filepath
# And output just codes to another file.
just_codes_results_filename = time.strftime("results_just_codes-%Y%m%d-%H%M%S.csv")
just_codes_results_filepath = os.path.join(out_directory, just_codes_results_filename)
export_results(codes, rows, just_codes_results_filepath)
print "Exported just code results to " + just_codes_results_filepath
warn_about_bad_group_lengths(groups, plant_spacing)
# KLM - not reliable since not all numbers were actually planted
#warn_about_missing_single_codes(single_code_ids)
warn_about_missing_single_code_lengths(single_segments, plant_spacing)
if not os.path.exists(out_directory):
os.makedirs(out_directory)
# Write averaged results out to file.
import time
from src.processing.export_results import export_results
from src.util.numbering import number_serpentine
avg_results_filename = time.strftime("_results_averaged-%Y%m%d-%H%M%S.csv")
avg_results_filepath = os.path.join(out_directory, avg_results_filename)
sorted_rows = sorted(rows, key=lambda r: r.number)
items = number_serpentine(sorted_rows)
export_results(items, [], avg_results_filepath)
print 'Output codes to {}'.format(avg_results_filepath)
# extra_ids = ['123', '302', '488', '645', '647']
# extra_groups = [group for group in groups if group.id in extra_ids]
# for group in extra_groups:
# length = group.length
#
# expected_plants = length / plant_spacing
# print "Group ID {} has {} expected plants".format(group.id, expected_plants)
