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extract_individual_truck_data.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import more_itertools as mit
import csv
import glob
import os
path = r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\tph_grade_Oct_Mar'
list_sorted_path_ordered_csv1 = []
list_sorted_path_ordered_csv2 = []
###2021 10-12
for k in range(1,2,1):
for j in range(1,2,1):
#for i in range(1,10,1):
for i in range(0,3,1):
if j==1 and i>2:
break
else:
pattern = '/[A-Z_]*[0-9][D][' +str(j) +']['+str(i)+'][M]' + '[2][0][2][' +str(k) +']Y*.csv'
pathall = str('%s%s'%(path,pattern))
sorted_path = glob.glob(pathall)
print(k,j,i)
#print(sorted_path)
list_sorted_path_ordered_csv1.extend(sorted_path)
list_sorted_path_ordered_csv2.extend(sorted_path)
###2022 01-present(03)
for k in range(2,3,1):
for j in range(0,2,1):
#for i in range(1,10,1):
for i in range(1,9,1):
if j==1 and i>2:
break
else:
pattern = '/[A-Z_]*[0-9][D][' +str(j) +']['+str(i)+'][M]' + '[2][0][2][' +str(k) +']Y*.csv'
pathall = str('%s%s'%(path,pattern))
sorted_path = glob.glob(pathall)
print(k,j,i)
#print(sorted_path)
list_sorted_path_ordered_csv1.extend(sorted_path)
list_sorted_path_ordered_csv2.extend(sorted_path)
###########tonnage#############
# list_sorted_path_ordered_csv2 = [w.replace('tph_grade_Oct_Mar','individual_truck_tonnage') for w in list_sorted_path_ordered_csv1]
# all_tonnage = []
# count=0
# for item1,item2 in zip(list_sorted_path_ordered_csv1,list_sorted_path_ordered_csv2):
# data = pd.read_csv(item1, on_bad_lines='skip')
# TPH = data.TPH.to_list()
# GRADE = data.GRADE.to_list()
# index = list(range(len(TPH)))
# list1 = []
# list_extracted = []
# group_list1 =[]
# group_list2 =[]
# min_interval=75
# max_interval=112
# list1.append(index)
# list1.append(TPH)
# list1.append(GRADE)
# data = np.array(list1)
# Tonnage = data[1]
# Grade = data[2]
# idx = np.where(Tonnage>800)
# list_extracted.append(idx)
# list_extracted.append(Tonnage[Tonnage>800])
# #print(list_extracted[0][0],list_extracted[1])
# for group in mit.consecutive_groups(list_extracted[0][0]):
# group_list1.append(list(group))
# for i in range(len(group_list1)):
# if min_interval-1<len(group_list1[i])<max_interval-1:
# group_list2.append(group_list1[i])
# else:
# pass
# filepath1 = item2
# with open(filepath1, "w",newline='') as file1:
# writer=csv.writer(file1)
# #header=['Individual truck grade between 2min to 6 min ']
# #writer.writerow(header)
# #writer.writerow(list1[0:][1])
# for i in range(len(group_list2)):#
# selected_tonnage = []
# selected_grade = []
# #print(group_list2[0:][i])
# for item in group_list2[0:][i]:
# if list1[2][item] ==0:
# pass
# else:
# selected_tonnage.append(list1[1][item])
# writer.writerow(selected_tonnage)
# all_tonnage.append(selected_tonnage)
###########grade#############
list_sorted_path_ordered_csv3 = [w.replace('tph_grade_Oct_Mar','individual_truck_grade') for w in list_sorted_path_ordered_csv1]
all_grade = []
count=0
for item1,item2 in zip(list_sorted_path_ordered_csv1,list_sorted_path_ordered_csv3):
data = pd.read_csv(item1, on_bad_lines='skip')
TPH = data.TPH.to_list()
GRADE = data.GRADE.to_list()
index = list(range(len(TPH)))
list1 = []
list_extracted = []
group_list1 =[]
group_list2 =[]
min_interval=75
max_interval=112
list1.append(index)
list1.append(TPH)
list1.append(GRADE)
data = np.array(list1)
Tonnage = data[1]
Grade = data[2]
idx = np.where(Tonnage>800)
list_extracted.append(idx)
list_extracted.append(Tonnage[Tonnage>800])
#print(list_extracted[0][0],list_extracted[1])
for group in mit.consecutive_groups(list_extracted[0][0]):
group_list1.append(list(group))
for i in range(len(group_list1)):
if min_interval-1<len(group_list1[i])<max_interval-1:
group_list2.append(group_list1[i])
else:
pass
filepath1 = item2
with open(filepath1, "w",newline='') as file1:
writer=csv.writer(file1)
header=['Individual truck grade between 2min to 6 min ']
writer.writerow(header)
#writer.writerow(list1[0:][1])
for i in range(len(group_list2)):#
selected_tonnage = []
selected_grade = []
#print(group_list2[0:][i])
for item in group_list2[0:][i]:
if list1[2][item] ==0:
pass
else:
selected_grade.append((list1[2][item])/100)
writer.writerow(selected_grade)
all_grade.append(selected_grade)
###rename
# string_list = []
# for i in list_sorted_path_ordered_csv1:
# #print(i[-15:-13]) #day
# #print(i[-12:-10]) #month
# #print(i[-9:-5]) #year
# string_list.append(i[-9:-5] + '-'+ i[-12:-10] + '-'+ i[-15:-13])
import os
path1 = os.listdir('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\')
for i in path1:
os.rename('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\'+ i, 'C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\'+i[-9:-5] + '-'+ i[-12:-10] + '-'+ i[-15:-13] +'.csv')
# df11 = pd.read_csv(r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\tph_grade_Oct_Mar - Copy\PLC_CMP_Data_For13D10M2021Y.csv')
# df22 = pd.read_csv(r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\tph_grade_Oct_Mar\2021-10-13.csv')
# df11.equals(df22)
# all_grade_no_zero = []
# all_tonnage_no_zero = []
# for i in range(len(all_grade)):
# if len(all_grade[i]) ==0:
# pass
# else:
# all_grade_no_zero.append(all_grade[i])
# all_tonnage_no_zero.append(all_tonnage[i])
# with open(r"C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\2min_6min_time_elapse_individual_truck_tonnage_Oct21_Mar22.csv", "w",newline='') as file2:
# writer=csv.writer(file2)
# header=['Individual truck tonnage between 2min to 5 min time elapse']
# writer.writerow(header)
# writer.writerows(all_tonnage_no_zero)
#print(list1[1][i])
# with open(r"C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\2min_6min_time_elapse_individual_truck_grade_Oct21_Mar22.csv", "w",newline='') as file3:
# writer=csv.writer(file3)
# header=['Individual truck grade between 2min to 5 min time elapse']
# writer.writerow(header)
# writer.writerows(all_grade_no_zero)
# # Tonnage_each_truck= []
# # for i in range(len(all_tonnage_no_zero)):
# # print((sum(all_tonnage_no_zero[0:][i])/len(all_tonnage_no_zero[0:][i]))*(len(all_tonnage_no_zero[0:][i]))/60/15)
# # values = (sum(all_tonnage_no_zero[0:][i])/len(all_tonnage_no_zero[0:][i]))*(len(all_tonnage_no_zero[0:][i]))/60/15
# # Tonnage_each_truck.append(values)
# # Grade_each_truck = []
# # for i in range(len(all_grade_no_zero)):
# # #print((sum(all_grade[0:][i])/len(all_grade[0:][i])))
# # if len(all_grade_no_zero[0:][i])==0:
# # pass
# # else:
# # values = sum(all_grade_no_zero[0:][i])/len(all_grade_no_zero[0:][i])
# # Grade_each_truck.append(values)
# # import matplotlib.pyplot as plt
# # plt.hist(Tonnage_each_truck,100)
# # plt.xlabel('Average tonnage on individual truck (ton)',fontsize=16)
# # plt.ylabel('Numbers of individual truck',fontsize=16)
# # plt.show()
# # import matplotlib.pyplot as plt
# # plt.hist(Grade_each_truck,100)
# # plt.xlabel('Average grade on individual truck (%)',fontsize=16)
# # plt.ylabel('Numbers of individual truck',fontsize=16)
# # plt.show()
# #print(group_list2)
# #
# #print(i)
# #
# # group_list.remove(group_list[i])
# #print(i,len(group_list[i]))
# # if len(group_list[i])<10:
# # pass
# # else:
# # group_list.remove(group_list[i])
# #############################################################
#Extract all grade over 2000tonnage and average grade each day over 2000tonnage
# import numpy as np
# import matplotlib.pyplot as plt
# import pandas as pd
# import more_itertools as mit
# import csv
# import glob
# import os
# path = r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\tph_grade_Oct21_Mar22'
# list_sorted_path_ordered_csv1 = []
# list_sorted_path_ordered_csv2 = []
# ###2021 10-12
# for k in range(1,2,1):
# for j in range(1,2,1):
# #for i in range(1,10,1):
# for i in range(0,3,1):
# if j==1 and i>2:
# break
# else:
# pattern = '/[A-Z_]*[0-9][D][' +str(j) +']['+str(i)+'][M]' + '[2][0][2][' +str(k) +']Y*.csv'
# pathall = str('%s%s'%(path,pattern))
# sorted_path = glob.glob(pathall)
# print(k,j,i)
# #print(sorted_path)
# list_sorted_path_ordered_csv1.extend(sorted_path)
# list_sorted_path_ordered_csv2.extend(sorted_path)
# ###2022 01-present(03)
# for k in range(2,3,1):
# for j in range(0,2,1):
# #for i in range(1,10,1):
# for i in range(1,9,1):
# if j==1 and i>2:
# break
# else:
# pattern = '/[A-Z_]*[0-9][D][' +str(j) +']['+str(i)+'][M]' + '[2][0][2][' +str(k) +']Y*.csv'
# pathall = str('%s%s'%(path,pattern))
# sorted_path = glob.glob(pathall)
# print(k,j,i)
# #print(sorted_path)
# list_sorted_path_ordered_csv1.extend(sorted_path)
# list_sorted_path_ordered_csv2.extend(sorted_path)
# list_sorted_path_ordered_csv3 = [w.replace('tph_grade_Oct21_Mar22','grade_over_2000tonnage_each_row') for w in list_sorted_path_ordered_csv1]
# all_grade = []
# average_grade_per_day = []
# count=0
# list_time = []
# for item1,item2 in zip(list_sorted_path_ordered_csv1,list_sorted_path_ordered_csv3):
# data = pd.read_csv(item1)
# TPH = data.TPH.to_list()
# grade = data.GRADE.to_list()
# index = list(range(len(TPH)))
# list1 = []
# list_extracted = []
# group_list1 =[]
# group_list2 =[]
# min_interval=1
# list1.append(index)
# list1.append(TPH)
# list1.append(grade)
# data = np.array(list1)
# Tonnage = data[1]
# Grade = data[2]
# idx = np.where(Tonnage>2000)
# list_extracted.append(idx)
# list_extracted.append(Tonnage[Tonnage>2000])
# #print(list_extracted[0][0],list_extracted[1])
# for group in mit.consecutive_groups(list_extracted[0][0]):
# group_list1.append(list(group))
# for i in range(len(group_list1)):
# if min_interval-1<len(group_list1[i]):
# group_list2.append(group_list1[i])
# else:
# pass
# filepath1 = item2
# each_csv_grade = []
# with open(filepath1, "w",newline='') as file1:
# writer=csv.writer(file1)
# header=['grade over 2000tonnage ']
# writer.writerow(header)
# #writer.writerow(list1[0:][1])
# for i in range(len(group_list2)):#
# selected_tonnage = []
# selected_grade = []
# #print(group_list2[0:][i])
# for item in group_list2[0:][i]:
# if list1[2][item] ==0:
# pass
# else:
# selected_grade.append((list1[2][item])/100)
# each_csv_grade.append((list1[2][item])/100)
# writer.writerow(selected_grade)
# all_grade.append(selected_grade)
# average_grade_per_day.append(np.mean(np.array(each_csv_grade)))
# list_time.append(item1[123:-4])
# with open(filepath1, "w",newline='') as file1:
# writer=csv.writer(file1)
# header=['grade over 2000tonnage ']
# writer.writerow(header)
# #writer.writerow(list1[0:][1])
# for i in range(len(group_list2)):#
# selected_tonnage = []
# selected_grade = []
# #print(group_list2[0:][i])
# for item in group_list2[0:][i]:
# if list1[2][item] ==0:
# pass
# else:
# selected_grade.append((list1[2][item])/100)
# each_csv_grade.append((list1[2][item])/100)
# writer.writerow(selected_grade)
# all_grade.append(selected_grade)
# average_grade_per_day.append(np.mean(np.array(each_csv_grade)))
# list_time.append(item1[123:-4])
# all_grade_no_zero = []
# for i in range(len(all_grade)):
# if len(all_grade[i]) ==0:
# pass
# else:
# all_grade_no_zero.append(all_grade[i])
# all_grade_no_zero_concat = [j for i in all_grade_no_zero for j in i]
# ###save all grade over 2000 tonnage
# with open(r"C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\all_grade_over_2000tonnage.csv", "w",newline='') as file3:
# writer=csv.writer(file3)
# header=['all grade over 2000tonnage']
# writer.writerow(header)
# writer.writerows(map(lambda x: [x],all_grade_no_zero_concat))
# ###save average grade per day
# with open(r"C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\all_grade_per_day.csv", "w",newline='') as file3:
# writer=csv.writer(file3,delimiter=",")
# writer.writerow(('average grade per day','time'))
# writer.writerows(zip(list_time,average_grade_per_day))
# data = pd.read_csv(r"C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\all_grade_over_2000tonnage.csv")
# idx = len(data) - 1 if len(data) % 3 else len(data)
# data2 = data[:idx].groupby(data.index[:idx]//2).mean()
# data3 = data[:idx].groupby(data.index[:idx]//3).mean()
# data4 = data[:idx].groupby(data.index[:idx]//4).mean()
# data50 = data[:idx].groupby(data.index[:idx]//50).mean()
# Tonnage_each_truck= []
# for i in range(len(all_tonnage_no_zero)):
# print((sum(all_tonnage_no_zero[0:][i])/len(all_tonnage_no_zero[0:][i]))*(len(all_tonnage_no_zero[0:][i]))/60/15)
# values = (sum(all_tonnage_no_zero[0:][i])/len(all_tonnage_no_zero[0:][i]))*(len(all_tonnage_no_zero[0:][i]))/60/15
# Tonnage_each_truck.append(values)
# Grade_each_truck = []
# for i in range(len(all_grade_no_zero)):
# #print((sum(all_grade[0:][i])/len(all_grade[0:][i])))
# if len(all_grade_no_zero[0:][i])==0:
# pass
# else:
# values = sum(all_grade_no_zero[0:][i])/len(all_grade_no_zero[0:][i])
# Grade_each_truck.append(values)
# import matplotlib.pyplot as plt
# plt.hist(Tonnage_each_truck,100)
# plt.xlabel('Average tonnage on individual truck (ton)',fontsize=16)
# plt.ylabel('Numbers of individual truck',fontsize=16)
# plt.show()
# import matplotlib.pyplot as plt
# plt.hist(Grade_each_truck,100)
# plt.xlabel('Average grade on individual truck (%)',fontsize=16)
# plt.ylabel('Numbers of individual truck',fontsize=16)
# plt.show()
# print(group_list2)
#
#print(i)
#
# group_list.remove(group_list[i])
#print(i,len(group_list[i]))
# if len(group_list[i])<10:
# pass
# else:
# group_list.remove(group_list[i])
############################grade over 2000tonnage for each day################################
# import numpy as np
# import matplotlib.pyplot as plt
# import pandas as pd
# import more_itertools as mit
# import csv
# import glob
# import os
# path = r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Trial Data - Copy\tph_grade_Oct21_Mar22'
# list_sorted_path_ordered_csv1 = []
# list_sorted_path_ordered_csv2 = []
# ###2021 10-12
# for k in range(1,2,1):
# for j in range(1,2,1):
# #for i in range(1,10,1):
# for i in range(0,3,1):
# if j==1 and i>2:
# break
# else:
# pattern = '/[A-Z_]*[0-9][D][' +str(j) +']['+str(i)+'][M]' + '[2][0][2][' +str(k) +']Y*.csv'
# pathall = str('%s%s'%(path,pattern))
# sorted_path = glob.glob(pathall)
# print(k,j,i)
# #print(sorted_path)
# list_sorted_path_ordered_csv1.extend(sorted_path)
# list_sorted_path_ordered_csv2.extend(sorted_path)
# ###2022 01-present(03)
# for k in range(2,3,1):
# for j in range(0,2,1):
# #for i in range(1,10,1):
# for i in range(1,9,1):
# if j==1 and i>2:
# break
# else:
# pattern = '/[A-Z_]*[0-9][D][' +str(j) +']['+str(i)+'][M]' + '[2][0][2][' +str(k) +']Y*.csv'
# pathall = str('%s%s'%(path,pattern))
# sorted_path = glob.glob(pathall)
# print(k,j,i)
# #print(sorted_path)
# list_sorted_path_ordered_csv1.extend(sorted_path)
# list_sorted_path_ordered_csv2.extend(sorted_path)
# list_sorted_path_ordered_csv3 = [w.replace('tph_grade_Oct21_Mar22','grade_over_2000tonnage_each_row_tonnage') for w in list_sorted_path_ordered_csv1]
# all_grade = []
# average_grade_per_day = []
# count=0
# list_time = []
# for item1,item2 in zip(list_sorted_path_ordered_csv1,list_sorted_path_ordered_csv3):
# data = pd.read_csv(item1)
# TPH = data.TPH.to_list()
# grade = data.GRADE.to_list()
# index = list(range(len(TPH)))
# list1 = []
# list_extracted = []
# group_list1 =[]
# group_list2 =[]
# min_interval=1
# list1.append(index)
# list1.append(TPH)
# list1.append(grade)
# data = np.array(list1)
# Tonnage = data[1]
# Grade = data[2]
# idx = np.where(Tonnage>2000)
# list_extracted.append(idx)
# list_extracted.append(Tonnage[Tonnage>2000])
# #print(list_extracted[0][0],list_extracted[1])
# for group in mit.consecutive_groups(list_extracted[0][0]):
# group_list1.append(list(group))
# for i in range(len(group_list1)):
# if min_interval-1<len(group_list1[i]):
# group_list2.append(group_list1[i])
# else:
# pass
# filepath1 = item2
# each_csv_grade = []
# with open(filepath1, "w",newline='') as file1:
# writer=csv.writer(file1)
# header=['grade over 2000tonnage ']
# writer.writerow(header)
# #writer.writerow(list1[0:][1])
# for i in range(len(group_list2)):#
# selected_tonnage = []
# selected_grade = []
# #print(group_list2[0:][i])
# for item in group_list2[0:][i]:
# if list1[2][item] ==0:
# pass
# else:
# selected_grade.append((list1[2][item])/100)
# each_csv_grade.append((list1[2][item])/100)
# writer.writerow(selected_grade)
# all_grade.append(selected_grade)
# average_grade_per_day.append(np.mean(np.array(each_csv_grade)))
# flat_group_list2 = [x for xs in group_list2 for x in xs]
# list3 = []
# list4 = []
# for i in flat_group_list2:
# list3.append((list1[2][i])/100)
# list4.append((list1[1][i]))
# # with open(filepath1, "w",newline='') as file1:
# # writer=csv.writer(file1, delimiter = '\n')
# # header=['grade over 2000tonnage ']
# # writer.writerow(header)
# # writer.writerow(list3)
# with open(filepath1, "w",newline='') as file1:
# writer=csv.writer(file1, delimiter = '\n')
# header=['tonnage over 2000tonnage ']
# writer.writerow(header)
# writer.writerow(list4)