File size: 7,239 Bytes
ecd6340 | 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 | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import json
import os
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor
# disable the warning from pandas
pd.options.mode.chained_assignment = None
id_info = json.load(open('id_info.json'))
id_list = list(id_info.keys())
print('Reading data...')
dfs = {}
for id in id_list:
dfs[id] = pd.read_parquet(f'./slim_data/id_{id}.parquet')
sensor_downtimes = {}
for id in tqdm(id_list):
tqdm.write(f"Preparing ID: {id}")
df = dfs[str(id)].copy()
df['DATA_AS_OF'] = pd.to_datetime(df['DATA_AS_OF'])
# round the DATA_AS_OF to the nearest 5 minutes
df['DATA_AS_OF'] = df['DATA_AS_OF'].dt.round('5min')
tqdm.write(f"De-duplicating ID: {id}")
#find duplicate timestamps
missing_gap0 = df['DATA_AS_OF'].diff().dt.total_seconds() == 0
# missing_gap5 = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
missing_gap0 = missing_gap0[missing_gap0].index
to_remove=[]
for ind in missing_gap0:
# if the DATA_AS_OF of ind is 10min smaller than ind+1 then add 5 min to ind
if ind+1 == len(df):
to_remove.append(ind)
elif df['DATA_AS_OF'].iloc[ind] + pd.Timedelta('10min') == df['DATA_AS_OF'].iloc[ind+1]:
# print(f'adjust {ind}')
df['DATA_AS_OF'].iloc[ind] = df['DATA_AS_OF'].iloc[ind] + pd.Timedelta('5min')
else:
# remove the row of ind
to_remove.append(ind)
df = df.drop(to_remove)
df = df.reset_index(drop=True)
# check missing_gap0 again
missing_gap0 = df['DATA_AS_OF'].diff().dt.total_seconds() == 0
missing_gap0 = missing_gap0[missing_gap0].index
assert len(missing_gap0) == 0, 'There are still duplicate timestamps'
tqdm.write(f"Small gaps ID: {id}")
# get the missing gaps of 5~2h
threshold_time = 120 # in min
missing_gap15 = df['DATA_AS_OF'].diff().dt.total_seconds() <= (60*threshold_time) # make it 2h
missing_gap5 = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
missing_gap = missing_gap15 & missing_gap5
missing_gap = missing_gap[missing_gap].index
def linear_impute(start_idx, end_idx):
start_time = df['DATA_AS_OF'][start_idx]
end_time = df['DATA_AS_OF'][end_idx]
start_speed = df['SPEED'][start_idx]
end_speed = df['SPEED'][end_idx]
start_travel_time = df['TRAVEL_TIME'][start_idx]
end_travel_time = df['TRAVEL_TIME'][end_idx]
gap = end_time - start_time
gap = gap.total_seconds()
new_rows = []
for j in range(1, int(gap // 300)):
new_rows.append({
'DATA_AS_OF': start_time + pd.Timedelta(f'{j*5}min'),
'SPEED': start_speed + (end_speed - start_speed) * j / (gap // 300),
'TRAVEL_TIME': start_travel_time + (end_travel_time - start_travel_time) * j / (gap // 300)
})
return new_rows
with ThreadPoolExecutor(max_workers=100) as executor:
futures = [executor.submit(linear_impute, i - 1, i) for i in missing_gap]
results = [future.result() for future in tqdm(futures)]
# Flatten the list of lists
new_rows = [item for sublist in results for item in sublist]
# Create a DataFrame from the new rows and concatenate it with the original DataFrame
new_df = pd.DataFrame(new_rows)
df = pd.concat([df, new_df], ignore_index=True)
# sort by the DATA_AS_OF
df = df.sort_values('DATA_AS_OF')
df = df.reset_index(drop=True)
# check again
missing_gap15 = df['DATA_AS_OF'].diff().dt.total_seconds() <= (60*threshold_time)
missing_gap5 = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
missing_gap = missing_gap15 & missing_gap5
missing_gap = missing_gap[missing_gap].index
assert len(missing_gap) == 0, 'There are still missing gaps'
tqdm.write(f"Large gaps ID: {id}")
missing_gaplarge = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*threshold_time) # make it 2h #30 min is ok
missing_gaplarge = missing_gaplarge[missing_gaplarge].index
def zero_impute(start_idx, end_idx):
start_time = df['DATA_AS_OF'][start_idx]
end_time = df['DATA_AS_OF'][end_idx]
gap = end_time - start_time
gap = gap.total_seconds()
new_rows = []
for j in range(1, int(gap // 300)):
new_rows.append({
'DATA_AS_OF': start_time + pd.Timedelta(f'{j*5}min'),
'SPEED': 0,
'TRAVEL_TIME': 0
})
return new_rows
with ThreadPoolExecutor(max_workers=100) as executor:
futures = [executor.submit(zero_impute, i - 1, i) for i in missing_gaplarge]
results = [future.result() for future in tqdm(futures)]
# Flatten the list of lists
new_rows = [item for sublist in results for item in sublist]
# Create a DataFrame from the new rows and concatenate it with the original DataFrame
new_df = pd.DataFrame(new_rows)
df = pd.concat([df, new_df], ignore_index=True)
# sort by the DATA_AS_OF
df = df.sort_values('DATA_AS_OF')
df = df.reset_index(drop=True)
# check again
missing_anygap = df['DATA_AS_OF'].diff().dt.total_seconds() > (60*5)
missing_anygap = missing_anygap[missing_anygap].index
assert len(missing_anygap) == 0, 'There are still missing gaps'
tqdm.write(f"Sensor downtime ID: {id}")
# get sensor downtime
# get all the SPEED=0
zero_speed = df['SPEED']==0
speed_goes_down = df['SPEED'].diff() < 0
speed_goes_up = df['SPEED'].diff(-1) < 0
speed_goto_zero = zero_speed & speed_goes_down
speed_goto_zero = speed_goto_zero[speed_goto_zero].index
speed_gofrom_zero = zero_speed & speed_goes_up
speed_gofrom_zero = speed_gofrom_zero[speed_gofrom_zero].index
threshold_step = threshold_time//5
sensor_downtime = {}
i=0
for start, end in zip(speed_goto_zero, speed_gofrom_zero):
if end - start > threshold_step:
sensor_downtime[i] = {'time':(df['DATA_AS_OF'][start], df['DATA_AS_OF'][end]), 'index':(start, end)}
i+=1
# if the downtime is between 0:00 to 6:00 then remove the downtime from dictionary
def check_22_6(time):
if time.hour >= 0 and time.hour < 6:
return True
elif time.hour >=22:
return True
else:
return False
for key in list(sensor_downtime.keys()):
if check_22_6(sensor_downtime[key]['time'][0]) and check_22_6(sensor_downtime[key]['time'][1]):
del sensor_downtime[key]
# convert the 'time' segment to string
for key in sensor_downtime.keys():
sensor_downtime[key]['time'] = (str(sensor_downtime[key]['time'][0]), str(sensor_downtime[key]['time'][1]))
sensor_downtime = dict(enumerate(sensor_downtime.values()))
df.to_parquet(f'./impute_data/id_{id}.parquet')
sensor_downtimes[id] = sensor_downtime
id_info[str(id)]['sensor_downtime'] = sensor_downtime
id_info[str(id)]['len'] = len(df)
json.dump(id_info, open('./impute_data/id_info_imputed.json', 'w'), indent=4)
json.dump(sensor_downtimes, open('./impute_data/sensor_downtimes.json', 'w'), indent=4)
print('Done!')
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