-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtime transfer.py
325 lines (264 loc) · 12.5 KB
/
time transfer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import plotly.io as pio
import plotly.graph_objs as go
import plotly.express as px
import more_itertools as mit
import csv
import glob
import os
from datetime import datetime
pio.renderers.default='browser'
path = r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Kansanshi MRA Time'
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)
list_sorted_path_ordered_csv2 = [w.replace('Kansanshi MRA Time','Kansanshi MRA Time1') for w in list_sorted_path_ordered_csv1]
df1_sub = []
for i,j in zip(list_sorted_path_ordered_csv1,list_sorted_path_ordered_csv2):
df1 = pd.read_csv(str(i),names=['hour','minute','second','tonnage','grade'])
df1['timestamp'] = df1['hour']*3600 + df1['minute']*60 + df1['second']
df1['time'] = pd.to_datetime(df1["timestamp"], unit='s').dt.strftime("%H:%M:%S")
df1['datetime'] = i[-9:-5] + '-' + i[-12:-10] + '-' + i[-15:-13] + ' ' + df1['time']
df1['datetime'] = pd.to_datetime(df1['datetime'])
df1['date'] = i[-9:-5] + '-' + i[-12:-10] + '-' + i[-15:-13]
df1 = df1.drop(['hour','minute','second'],axis=1)
df1 = df1.iloc[:,[4,5,3,2,0,1]]
df1['datetime'] = df1['datetime'].astype(str)
df1_sub.append(df1)
#df1.to_csv(j,index=False)
df1_alldata = pd.concat(df1_sub)
df1_date = df1_alldata['date'].unique()
####stockpile
df2 = pd.read_excel('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Kansanshi Bore Core and GPS\\TO GYRO 2 - Trucks with polygon x y z midpoint for Oct and Nov 2021 - Copy.xlsx')
df2 = df2.drop(['DATE','END_TS','DATE1','TIME1','TIMESEC'],axis=1)
df2['TIME1'] = df2['TIME'].astype(str)
df2 = df2[df2['LOAD_LOCATION_SNAME']=='FINGER_9']
df2 = df2.reset_index(drop=True)
#df2 = df2.dropna(subset=['MID_X'])
df2.insert(0, 'date', df2.TIME1.str[0:10])
df2.insert(1, 'time', pd.Series([val.time() for val in df2['TIME']]))
df2.insert(2, 'hour', df2.TIME1.str[10:13].astype(int))
df2.insert(3, 'minute', df2.TIME1.str[14:16].astype(int))
df2.insert(4, 'second', df2.TIME1.str[17:19].astype(int))
df2.insert(5, 'timestamp', df2['hour']*3600 + df2['minute']*60 + df2['second'])
df2 = df2.drop(['hour','minute','second','TIME1'],axis=1)
#df2 = df2[2194:]
df2 = df2[833:]
df2 = df2.reset_index(drop=True)
df2_sub = []
df2_date = df2['date'].unique()
for i in df2_date:
sub = df2[df2['date']==i]
df2_sub.append(sub)
df1_date = pd.DataFrame(df1_date)
df2_date = pd.DataFrame(df2_date)
date = df2_date.merge(df1_date)[0].tolist()
df11 = [] #MR
df22 = [] #truck
for i in date:
sub1 = df1_alldata[df1_alldata['date']==i]
sub2 = df2[df2['date']==i]
df11.append(sub1)
df22.append(sub2)
delay = [300,600,900,1200]#5mins(300s) 10mins(600s) 15mins(900s) 20mins(1200s)
#tonnage = 300 #250T = 6mins
for n in delay:
list2 = []
locations_all = []
for df_a, df_b in zip(df11,df22):
df_a = df_a.reset_index(drop=True)
df_b = df_b.reset_index(drop=True)
list1 = []
locations = []
for i in range(df_b.shape[0]):
timestamp = df_b['timestamp'][i:i+1].values[0]
location = df_a[(pd.to_numeric(df_a["timestamp"], errors='coerce')>(timestamp+n)) & (pd.to_numeric(df_a["timestamp"], errors='coerce')<(timestamp+n+300))]
locations.append(location)
if location.shape[0]>0:
list1.append(i)
list2.append(list1)
locations_all.append(locations)
exec(f'mean_truck_grade_{n}s = []')
for i in range(len(locations_all)):
for j in locations_all[i]:
if len(j)>0:
if j[j['grade']>0]['grade'].mean()>0:
globals()['mean_truck_grade_'+str(n)+'s'].append(j[j['grade']>0]['grade'].mean())
plt.figure(figsize=(20,12))
plt.hist(mean_truck_grade_300s,histtype='step',bins=50,label='300s delay')
plt.hist(mean_truck_grade_600s,histtype='step',bins=50,label='600s delay')
plt.hist(mean_truck_grade_900s,histtype='step',bins=50,label='900s delay')
plt.hist(mean_truck_grade_1200s,histtype='step',bins=50,label='1200s delay')
plt.legend(fontsize=24)
plt.xlabel('mean grade',fontsize=24)
plt.ylabel('frequency',fontsize=24)
plt.tick_params(axis='both', which='major', labelsize=24)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import plotly.io as pio
import plotly.graph_objs as go
import plotly.express as px
import more_itertools as mit
import csv
import glob
import os
from datetime import datetime
pio.renderers.default='browser'
path = r'C:\Users\NIU004\OneDrive - CSIRO\Desktop\Mineral sorting\Kansanshi\Kansanshi MRA Time'
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(sorted_path)
list_sorted_path_ordered_csv1.extend(sorted_path)
list_sorted_path_ordered_csv2.extend(sorted_path)
list_sorted_path_ordered_csv2 = [w.replace('Kansanshi MRA Time','Kansanshi MRA Time1') for w in list_sorted_path_ordered_csv1]
df1_sub = []
for i,j in zip(list_sorted_path_ordered_csv1,list_sorted_path_ordered_csv2):
df1 = pd.read_csv(str(i),names=['hour','minute','second','tonnage','grade'])
df1['timestamp'] = df1['hour']*3600 + df1['minute']*60 + df1['second']
df1['time'] = pd.to_datetime(df1["timestamp"], unit='s').dt.strftime("%H:%M:%S")
df1['datetime'] = i[-9:-5] + '-' + i[-12:-10] + '-' + i[-15:-13] + ' ' + df1['time']
df1['datetime'] = pd.to_datetime(df1['datetime'])
df1['date'] = i[-9:-5] + '-' + i[-12:-10] + '-' + i[-15:-13]
df1 = df1.drop(['hour','minute','second'],axis=1)
df1 = df1.iloc[:,[4,5,3,2,0,1]]
df1['datetime'] = df1['datetime'].astype(str)
df1_sub.append(df1)
#df1.to_csv(j,index=False)
df1_alldata = pd.concat(df1_sub)
df1_date = df1_alldata['date'].unique()
####stockpile
df2 = pd.read_excel('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Kansanshi Bore Core and GPS\\TO GYRO 2 - Trucks with polygon x y z midpoint for Oct and Nov 2021 - Copy.xlsx')
df2 = df2.drop(['DATE','END_TS','DATE1','TIME1','TIMESEC'],axis=1)
df2['TIME1'] = df2['TIME'].astype(str)
df2 = df2.reset_index(drop=True)
df2 = df2.dropna(subset=['MID_X'])
df2.insert(0, 'date', df2.TIME1.str[0:10])
df2.insert(1, 'time', pd.Series([val.time() for val in df2['TIME']]))
df2.insert(2, 'hour', df2.TIME1.str[10:13].astype(int))
df2.insert(3, 'minute', df2.TIME1.str[14:16].astype(int))
df2.insert(4, 'second', df2.TIME1.str[17:19].astype(int))
df2.insert(5, 'timestamp', df2['hour']*3600 + df2['minute']*60 + df2['second'])
df2 = df2.drop(['hour','minute','second','TIME1'],axis=1)
df2 = df2[2194:]
# df2 = df2[(pd.to_numeric(df2["MID_X"], errors='coerce')>=3000)& (pd.to_numeric(df2["MID_X"], errors='coerce')<4000)
# & (pd.to_numeric(df2["MID_Y"], errors='coerce')>=12500)& (pd.to_numeric(df2["MID_Y"], errors='coerce')<13000)]
df2 = df2[(pd.to_numeric(df2["TCU"], errors='coerce')>0)]
df2 = df2.reset_index(drop=True)
fig = px.scatter_3d(df2, x="MID_X",y="MID_Y",z="MID_Z",color="TCU")
fig.update_traces(marker_size=4)
fig.update_layout(font=dict(size=14))
fig.update_layout(scene_aspectmode='data')
fig.show()
df2_sub = []
df2_date = df2['date'].unique()
for i in df2_date:
sub = df2[df2['date']==i]
df2_sub.append(sub)
df1_date = pd.DataFrame(df1_date)
df2_date = pd.DataFrame(df2_date)
date = df2_date.merge(df1_date)[0].tolist()
df11 = [] #MR
df22 = [] #truck
for i in date:
sub1 = df1_alldata[df1_alldata['date']==i]
sub2 = df2[df2['date']==i]
df11.append(sub1)
df22.append(sub2)
delay = [300,600,900,1200]#5mins(300s) 10mins(600s) 15mins(900s) 20mins(1200s)
#tonnage = 300 #250T = 6mins
for n in delay:
list2 = []
locations_all = []
for df_a, df_b in zip(df11,df22):
df_a = df_a.reset_index(drop=True)
df_b = df_b.reset_index(drop=True)
list1 = []
locations = []
for i in range(df_b.shape[0]):
timestamp = df_b['timestamp'][i:i+1].values[0]
location = df_a[(pd.to_numeric(df_a["timestamp"], errors='coerce')>(timestamp+n)) & (pd.to_numeric(df_a["timestamp"], errors='coerce')<(timestamp+n+300))]
locations.append(location)
if location.shape[0]>0:
list1.append(i)
list2.append(list1)
locations_all.append(locations)
exec(f'mean_truck_grade_{n}s = []')
for i in range(len(locations_all)):
for j in locations_all[i]:
if len(j)>0:
if j[j['grade']>0]['grade'].mean()>0:
globals()['mean_truck_grade_'+str(n)+'s'].append(j[j['grade']>0]['grade'].mean())
#####block data
df2_copy = df2
df2_copy = df2_copy.drop(['time','timestamp','TIME','WET_TONNES','TONNES'],axis=1)
df2_copy_unique = df2_copy.drop_duplicates(subset=['BLOCK_SNAME'],keep='first')
path1 = list(os.listdir('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\'))
path2 = [ x[:-4] for x in path1]
def common_elements(list1, list2):
result = []
for element in list1:
if element in list2:
result.append(element)
return result
same_list = common_elements(path2,list(df2_date[0]))
####pseudo truck data
pseudo_truck = []
for i in same_list:
print('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\'+i+'.csv')
pseudo_truck.append(pd.read_csv('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\'+i+'.csv',sep=" "))
pseudo_truck1 = []
for j in pseudo_truck:
for i in range(len(j)):
pseudo_truck1.append(j['Individual'][i].split(','))
pseudo_truck3 = []
for i in pseudo_truck1:
if len(i)>75:
pseudo_truck2 = []
for j in i:
pseudo_truck2.append(float(j))
pseudo_truck3.append(pseudo_truck2)
pseudo_truck3_mean = [np.mean(x) for x in pseudo_truck3]
a1 = pd.read_csv('C:\\Users\\NIU004\\OneDrive - CSIRO\\Desktop\\Mineral sorting\\Kansanshi\\Trial Data - Copy\\individual_truck_grade\\2021-10-26.csv',sep=" ")
variance_pseudo_truck3_mean = np.var(pseudo_truck3_mean) #truck scale - 200-300T
import itertools
pseudo_truck3_mra = list(itertools.chain(*pseudo_truck3))
plt.figure(figsize=(14,8))
# plt.hist(mean_truck_grade_300s,histtype='step', density=True,color = 'orange',bins=50,label='MRA - 300s delay')
# plt.hist(mean_truck_grade_600s,histtype='step', density=True,color = 'b',bins=50,label='MRA - 600s delay')
# plt.hist(mean_truck_grade_1200s,histtype='step', density=True,color = 'k',bins=50,label='MRA - 1200s delay')
#plt.hist(mean_truck_grade_900s,histtype='step', density=True,color = 'g',bins=50,label='MRA - 900s delay')
plt.hist(pseudo_truck3_mean,histtype='step', density=True,color = 'r',bins=50,label='pseudo truck')
# plt.hist(df2_copy_unique['TCU'],histtype='step', density=True,color = 'm',bins=50,label='block')
plt.hist(pseudo_truck3_mra,histtype='step', density=True,color = 'c',bins=50,label='MRA 4s')
plt.legend(fontsize=24)
plt.xlabel('mean grade',fontsize=24)
plt.ylabel('frequency',fontsize=24)
plt.tick_params(axis='both', which='major', labelsize=24)