#!/usr/bin/env python3 import pandas as pd import numpy as np # returns the value in the first column it finds that contains # the param name. (for a given row def getParam(param_name, row_indx, df_in): for index, row in df_in.iterrows(): if (index == row_indx): for column_name, value in row.items(): if (param_name in column_name): return(float(value)) print('ERROR: Could not find entry in df that contains' + str(param_name)) return(0.0) def findBatchStartAndChecks(df_in): # Find when all the batches start and # Check that the values are all the same accross all # the vehicles. batch_start_indx = [] last_all_input_gain = -111111.0 last_search_value_imprv_gain = -111111.0 last_batt_exh_gain = -111111111.0 last_explore_bias = -111111111.0 for index, row in df_in.iterrows(): new_batch_started_this_row = False all_vehicles_same_params = True this_row_all_input_gain = 0.0 this_row_search_value_imprv_gain = 0.0 this_row_batt_exh_gain = 0.0 this_row_explore_bias = 0.0 found_all_input_gain = False found_search_value_imprv_gain = False found_batt_exh_gain = False found_explore_bias = False for column_name, value in row.items(): if ('ALL_INPUT_GAIN' in column_name): # is this the first time we've seen this # var in this row? if (not found_all_input_gain): found_all_input_gain = True this_row_all_input_gain = float(value) # if not, is this a new value? elif (float(value) != this_row_all_input_gain): all_vehicles_same_params = False elif ('SEARCH_VALUE_IMPRV_GAIN' in column_name): # is this the first time we've seen this # var in this row? if (not found_search_value_imprv_gain): found_search_value_imprv_gain = True this_row_search_value_imprv_gain = float(value) # if not, is this a new value? elif (float(value) != this_row_search_value_imprv_gain): all_vehicles_same_params = False elif ('BATT_EXH_GAIN' in column_name): # is this the first time we've seen this # var in this row? if (not found_batt_exh_gain): found_batt_exh_gain = True this_row_batt_exh_gain = float(value) # if not, is this a new value? elif (float(value) != this_row_batt_exh_gain): all_vehicles_same_params = False elif ('EXPLORE_BIAS' in column_name): # is this the first time we've seen this # var in this row? if (not found_explore_bias): found_explore_bias = True this_row_explore_bias = float(value) # if not, is this a new value? elif (float(value) != this_row_explore_bias): all_vehicles_same_params = False # Ok, now check they all had same if (not all_vehicles_same_params): print('ERROR: found a run where vehicles did not have the same params!') print(' Bad run is index = ' + str(index)) else: if (this_row_all_input_gain != last_all_input_gain): new_batch_started_this_row = True if (this_row_search_value_imprv_gain != last_search_value_imprv_gain): new_batch_started_this_row = True if (this_row_batt_exh_gain != last_batt_exh_gain): new_batch_started_this_row = True if (this_row_explore_bias != last_explore_bias): new_batch_started_this_row = True if (new_batch_started_this_row): print('Found new batch start at index = ' + str(index)) batch_start_indx.append(index) last_all_input_gain = this_row_all_input_gain last_search_value_imprv_gain = this_row_search_value_imprv_gain last_batt_exh_gain = this_row_batt_exh_gain last_explore_bias = this_row_explore_bias # Now we know where each batch starts and the data is clean! return batch_start_indx, all_vehicles_same_params def computeCosts(df_in): costs = [] # compute these metrics dist_per_sample = 0.0 # total odom for the fleet divided by the total samples odom_variance = 0.0 percent_unobserved_blooms = 0.0 percent_unsampled_blooms = 0.0 # compute for index, row in df_in.iterrows(): odoms = [] samples = [] detected_blooms_completed = 0.0 sampled_blooms_completed = 0.0 unsamp_undetect_blooms_completed = 0.0 for column_name, value in row.items(): if ('ODOM' in column_name): odoms.append(float(value)) elif ('NUMBER_OF_SAMPLED_BLOOMS' in column_name): samples.append(float(value)) elif ('DETECTED_BLOOMS_COMPLETED' in column_name): detected_blooms_completed = float(value) elif ('SAMPLED_BLOOMS_COMPLETED' in column_name): sampled_blooms_completed = float(value) elif ('UNSAMP_UNDETECT_BLOOMS_COMPLETED' in column_name): unsamp_undetect_blooms_completed = float(value) # calculate cost for this row. total_odom = sum(odoms) total_samples = sum(samples) if (total_samples == 0): total_samples = 1.0 # just so we never divide by zero. This will be a huge # number anyway #------------------------------------------- dist_per_sample = total_odom / total_samples #print('dist_per_sample =' + str(dist_per_sample)) #------------------------------------------- odom_variance = np.var(odoms) #print('odom_variance =' + str(odom_variance)) total_blooms = detected_blooms_completed + sampled_blooms_completed + unsamp_undetect_blooms_completed if (total_blooms == 0): total_blooms = 1.0 #------------------------------------------- percent_unobserved_blooms = float(unsamp_undetect_blooms_completed/total_blooms) #print('percent_unobserved_blooms = ' + str(percent_unobserved_blooms)) #------------------------------------------- percent_unsampled_blooms = float(detected_blooms_completed/total_blooms) #print('percent_unsampled_blooms = ' + str(percent_unsampled_blooms)) # try to nomalize everything this_cost = 0.0 #this_cost += dist_per_sample / 100.0 #this_cost += odom_variance / 100000.0 #this_cost += percent_unobserved_blooms * 4.0 this_cost += percent_unsampled_blooms * 2.0 #print('this cost = ' + str(this_cost)) costs.append(this_cost) # get all return costs