mvanryn/VTScada-HistoricalTools
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Starting work on individual yearly directories.

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This commit is contained in:
Michael Van Ryn 2023-05-01 16:25:04 -06:00
parent 1abed2227e
commit a010ddd844
6 changed files with 2241 additions and 21 deletions
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82
main.py
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@ -3,7 +3,9 @@ import toml
import sys
import requests
from typing import List, Union
from datetime import datetime, timezone
from datetime import datetime, timezone, timedelta
# LIMITATIONS - 10k rows per VTScada query - reccommend limit to minimum 10 second intervals for daily values
# ----------------------
# Classes
@ -72,6 +74,11 @@ def compress_boolean(values: List[Union[int, float, None]]) -> List[Union[int, f
return compressed_values
def create_directory(path):
if not os.path.exists(path):
os.makedirs(path)
def print_text(text: str):
print(r'-------------------------------------------------------------------------------------------------------')
print(text)
@ -106,25 +113,42 @@ def query_vtscada_tag(historical_tag: HistoricalTag, ft_start_time: datetime, ft
def query_vtscada(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
ft_start_time = "'" + str(start_time.astimezone(timezone.utc)) + "'"
ft_end_time = "'" + str(end_time.astimezone(timezone.utc)) + "'"
for tag in historical_tags:
print(tag.name_source)
values = query_vtscada_tag(tag, ft_start_time, ft_end_time)
current_start_time = start_time
current_end_time = start_time + timedelta(days=1)
if values is None:
print("No values found")
else:
if tag.tag_type == "real" or tag.tag_type == "integer":
compressed_values = compress_and_scale_real(
values, tag.deadband, tag.scale_factor, tag.precision)
while current_start_time < end_time:
dir_path = output_path + start_time.year + \
"_" + start_time.month + start_time.day
create_directory(dir_path)
ft_start_time = "'" + \
str(current_start_time.astimezone(timezone.utc)) + "'"
ft_end_time = "'" + \
str(current_end_time.astimezone(timezone.utc)) + "'"
tag_mappings = []
for tag in historical_tags:
print(tag.name_source)
values = query_vtscada_tag(tag, ft_start_time, ft_end_time)
if values is None:
print("No values found")
else:
compressed_values = compress_boolean(values)
if tag.tag_type == "real" or tag.tag_type == "integer":
compressed_values = compress_and_scale_real(
values, tag.deadband, tag.scale_factor, tag.precision)
else:
compressed_values = compress_boolean(values)
output_file = output_path + \
tag.name_source.replace('\\', '_') + ".csv"
write_values_to_file(output_file, compressed_values)
output_file = tag.name_source.replace('\\', '_') + ".csv"
full_output_file = output_path + output_file
write_values_to_file(full_output_file, compressed_values)
tag_mappings.append((output_file, tag.name_dest))
write_tagmapping_to_file(output_path + "TagMapping.csv", tag_mappings)
def write_values_to_file(output_file: str, values: List[Union[int, float, None]]):
@ -132,8 +156,21 @@ def write_values_to_file(output_file: str, values: List[Union[int, float, None]]
csv_writer = csv.writer(csvfile)
for value_pair in values:
if value_pair[1] is not None:
csv_writer.writerow(value_pair)
timestamp, value = value_pair
if value is not None:
utc_dt = datetime.utcfromtimestamp(timestamp)
formatted_timestamp = utc_dt.strftime(
'%Y-%m-%d %H:%M:%S.%f')[:-3]
csv_writer.writerow([formatted_timestamp, value])
def write_tagmapping_to_file(output_file: str, tag_mappings: List[str]):
with open(output_file, mode='w', newline='', encoding='utf-8') as csvfile:
csv_writer = csv.writer(csvfile)
for mapping in tag_mappings:
csv_writer.writerow(mapping)
# read_tags()
# ----------------------
@ -182,14 +219,17 @@ realm_name = config['vtscada']['realm_name']
# timezone = pytz.timezone(system_timezone)
if len(sys.argv) == 2:
if len(sys.argv) == 4:
query_type = sys.argv[1]
year, month, day = map(int, str(sys.argv[2]).split("-"))
start_time = datetime(year, month, day, 0, 0, 0)
year, month, day = map(int, str(sys.argv[3]).split("-"))
end_time = datetime(year, month, day, 0, 0, 0)
print("")
historical_tags = read_tags(tags_path)
start_time = datetime(2023, 5, 1, 9, 0, 0)
end_time = datetime(2023, 5, 1, 23, 59, 59)
for tag in historical_tags:
print(tag)

4
output/TagMapping.csv Normal file
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@ -0,0 +1,4 @@
temp_old_value1.csv,temp\new_value1
temp_old_value2.csv,temp\new_value2
temp_old_value3.csv,temp\new_value3
temp_old_value4.csv,temp\new_value4
1 temp_old_value1.csv temp\new_value1
2 temp_old_value2.csv temp\new_value2
3 temp_old_value3.csv temp\new_value3
4 temp_old_value4.csv temp\new_value4

603
output/temp_old_value1.csv Normal file
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@ -0,0 +1,603 @@
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2023-05-01 20:25:00.000,7870.41
2023-05-01 20:26:40.000,7885.56
2023-05-01 20:28:20.000,7900.71
2023-05-01 20:30:00.000,7915.86
2023-05-01 20:31:40.000,7931.01
2023-05-01 20:33:20.000,7946.16
2023-05-01 20:35:00.000,7961.31
2023-05-01 20:36:40.000,7976.46
2023-05-01 20:38:20.000,7991.61
2023-05-01 20:40:00.000,8006.76
2023-05-01 20:41:40.000,8021.91
2023-05-01 20:43:20.000,8037.06
2023-05-01 20:45:00.000,8052.21
2023-05-01 20:46:40.000,8067.36
2023-05-01 20:48:20.000,8082.51
2023-05-01 20:50:00.000,8097.66
2023-05-01 20:51:40.000,8112.81
2023-05-01 20:53:20.000,8127.96
2023-05-01 20:55:00.000,8143.11
2023-05-01 20:56:40.000,8158.26
2023-05-01 20:58:20.000,8173.41
2023-05-01 21:00:00.000,8188.56
2023-05-01 21:01:40.000,8203.71
2023-05-01 21:03:20.000,8218.86
2023-05-01 21:05:00.000,8234.01
2023-05-01 21:06:40.000,8249.16
2023-05-01 21:08:20.000,8264.31
2023-05-01 21:10:00.000,8279.46
2023-05-01 21:11:40.000,8294.61
2023-05-01 21:13:20.000,8309.76
2023-05-01 21:15:00.000,8324.91
2023-05-01 21:16:40.000,8340.06
2023-05-01 21:18:20.000,8355.21
2023-05-01 21:20:00.000,8365.03
2023-05-01 21:21:40.000,8365.22
1 2023-05-01 18:00:00.000 21505.29
2 2023-05-01 18:01:40.000 6567.51
3 2023-05-01 18:03:20.000 6582.66
4 2023-05-01 18:05:00.000 6597.81
5 2023-05-01 18:06:40.000 6612.96
6 2023-05-01 18:08:20.000 6628.11
7 2023-05-01 18:10:00.000 6643.26
8 2023-05-01 18:11:40.000 6658.41
9 2023-05-01 18:13:20.000 6673.56
10 2023-05-01 18:15:00.000 6688.71
11 2023-05-01 18:16:40.000 6703.86
12 2023-05-01 18:18:20.000 6719.01
13 2023-05-01 18:20:00.000 6734.16
14 2023-05-01 18:21:40.000 6749.31
15 2023-05-01 18:23:20.000 6764.46
16 2023-05-01 18:25:00.000 6779.61
17 2023-05-01 18:26:40.000 6794.76
18 2023-05-01 18:28:20.000 6809.91
19 2023-05-01 18:30:00.000 6825.06
20 2023-05-01 18:31:40.000 6840.21
21 2023-05-01 18:33:20.000 6855.36
22 2023-05-01 18:35:00.000 6870.51
23 2023-05-01 18:36:40.000 6885.66
24 2023-05-01 18:38:20.000 6900.81
25 2023-05-01 18:40:00.000 6915.96
26 2023-05-01 18:41:40.000 6931.11
27 2023-05-01 18:43:20.000 6946.26
28 2023-05-01 18:45:00.000 6961.41
29 2023-05-01 18:46:40.000 6976.56
30 2023-05-01 18:48:20.000 6991.71
31 2023-05-01 18:50:00.000 7006.86
32 2023-05-01 18:51:40.000 7022.01
33 2023-05-01 18:53:20.000 7037.16
34 2023-05-01 18:55:00.000 7052.31
35 2023-05-01 18:56:40.000 7067.46
36 2023-05-01 18:58:20.000 7082.61
37 2023-05-01 19:00:00.000 7097.76
38 2023-05-01 19:01:40.000 7112.91
39 2023-05-01 19:03:20.000 7128.06
40 2023-05-01 19:05:00.000 7143.21
41 2023-05-01 19:06:40.000 7158.36
42 2023-05-01 19:08:20.000 7173.51
43 2023-05-01 19:10:00.000 7188.66
44 2023-05-01 19:11:40.000 7203.81
45 2023-05-01 19:13:20.000 7218.96
46 2023-05-01 19:15:00.000 7234.11
47 2023-05-01 19:16:40.000 7249.26
48 2023-05-01 19:18:20.000 7264.41
49 2023-05-01 19:20:00.000 7279.56
50 2023-05-01 19:21:40.000 7294.71
51 2023-05-01 19:23:20.000 7309.86
52 2023-05-01 19:25:00.000 7325.01
53 2023-05-01 19:26:40.000 7340.16
54 2023-05-01 19:28:20.000 7355.31
55 2023-05-01 19:30:00.000 7370.46
56 2023-05-01 19:31:40.000 7385.61
57 2023-05-01 19:33:20.000 7400.76
58 2023-05-01 19:35:00.000 7415.91
59 2023-05-01 19:36:40.000 7431.06
60 2023-05-01 19:38:20.000 7446.21
61 2023-05-01 19:40:00.000 7461.36
62 2023-05-01 19:41:40.000 7476.51
63 2023-05-01 19:43:20.000 7491.66
64 2023-05-01 19:45:00.000 7506.81
65 2023-05-01 19:46:40.000 7521.96
66 2023-05-01 19:48:20.000 7537.11
67 2023-05-01 19:50:00.000 7552.26
68 2023-05-01 19:51:40.000 7567.41
69 2023-05-01 19:53:20.000 7582.56
70 2023-05-01 19:55:00.000 7597.71
71 2023-05-01 19:56:40.000 7612.86
72 2023-05-01 19:58:20.000 7628.01
73 2023-05-01 20:00:00.000 7643.16
74 2023-05-01 20:01:40.000 7658.31
75 2023-05-01 20:03:20.000 7673.46
76 2023-05-01 20:05:00.000 7688.61
77 2023-05-01 20:06:40.000 7703.76
78 2023-05-01 20:08:20.000 7718.91
79 2023-05-01 20:10:00.000 7734.06
80 2023-05-01 20:11:40.000 7749.21
81 2023-05-01 20:13:20.000 7764.36
82 2023-05-01 20:15:00.000 7779.51
83 2023-05-01 20:16:40.000 7794.66
84 2023-05-01 20:18:20.000 7809.81
85 2023-05-01 20:20:00.000 7824.96
86 2023-05-01 20:21:40.000 7840.11
87 2023-05-01 20:23:20.000 7855.26
88 2023-05-01 20:25:00.000 7870.41
89 2023-05-01 20:26:40.000 7885.56
90 2023-05-01 20:28:20.000 7900.71
91 2023-05-01 20:30:00.000 7915.86
92 2023-05-01 20:31:40.000 7931.01
93 2023-05-01 20:33:20.000 7946.16
94 2023-05-01 20:35:00.000 7961.31
95 2023-05-01 20:36:40.000 7976.46
96 2023-05-01 20:38:20.000 7991.61
97 2023-05-01 20:40:00.000 8006.76
98 2023-05-01 20:41:40.000 8021.91
99 2023-05-01 20:43:20.000 8037.06
100 2023-05-01 20:45:00.000 8052.21
101 2023-05-01 20:46:40.000 8067.36
102 2023-05-01 20:48:20.000 8082.51
103 2023-05-01 20:50:00.000 8097.66
104 2023-05-01 20:51:40.000 8112.81
105 2023-05-01 20:53:20.000 8127.96
106 2023-05-01 20:55:00.000 8143.11
107 2023-05-01 20:56:40.000 8158.26
108 2023-05-01 20:58:20.000 8173.41
109 2023-05-01 21:00:00.000 8188.56
110 2023-05-01 21:01:40.000 8203.71
111 2023-05-01 21:03:20.000 8218.86
112 2023-05-01 21:05:00.000 8234.01
113 2023-05-01 21:06:40.000 8249.16
114 2023-05-01 21:08:20.000 8264.31
115 2023-05-01 21:10:00.000 8279.46
116 2023-05-01 21:11:40.000 8294.61
117 2023-05-01 21:13:20.000 8309.76
118 2023-05-01 21:15:00.000 8324.91
119 2023-05-01 21:16:40.000 8340.06
120 2023-05-01 21:18:20.000 8355.21
121 2023-05-01 21:20:00.000 8365.03
122 2023-05-01 21:21:40.000 8365.22

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