466 lines
16 KiB
Python
466 lines
16 KiB
Python
import csv
|
|
import toml
|
|
import sys
|
|
import os
|
|
import requests
|
|
import pyodbc
|
|
import subprocess
|
|
import time
|
|
from typing import List, Union
|
|
from datetime import datetime, timezone, timedelta
|
|
|
|
# LIMITATIONS - 10k rows per VTScada query - reccommend limit to minimum 10 second intervals for daily values
|
|
|
|
# ----------------------
|
|
# Classes
|
|
# ----------------------
|
|
|
|
# HistoricalTag
|
|
# ----------------------
|
|
|
|
|
|
class HistoricalTag:
|
|
def __init__(self,
|
|
row: int,
|
|
tag_type: str,
|
|
name_source: str,
|
|
name_dest: str,
|
|
scale_factor: float,
|
|
interval: int,
|
|
precision: int,
|
|
deadband: float):
|
|
self.row = row
|
|
self.tag_type = tag_type
|
|
self.name_source = name_source
|
|
self.name_dest = name_dest
|
|
self.scale_factor = scale_factor
|
|
self.interval = interval
|
|
self.precision = precision
|
|
self.deadband = deadband
|
|
|
|
def __repr__(self):
|
|
return f"({self.row}, {self.tag_type}, {self.name_source}, {self.name_dest}, {self.scale_factor}, {self.interval}, {self.precision}, {self.deadband})"
|
|
|
|
# ----------------------
|
|
# Functions
|
|
# ----------------------
|
|
|
|
# clearscada_generate_historical_ids()
|
|
# ----------------------
|
|
# Generates a list of historical IDs for found historic files
|
|
|
|
|
|
def clearscada_generate_historical_ids(historic_files: str):
|
|
ids = []
|
|
|
|
for directory in os.listdir(historic_files):
|
|
if os.fsdecode(directory).startswith("Historic "):
|
|
ids.append(int(directory[9:15]))
|
|
|
|
output_file = os.path.join(output_path, "CS_HistoricIDs.CSV")
|
|
|
|
with open(output_file, mode='w', newline='', encoding='utf-8') as csvfile:
|
|
csv_writer = csv.writer(csvfile)
|
|
|
|
for id in ids:
|
|
if id is not None:
|
|
csv_writer.writerow([str(id)])
|
|
|
|
# clearscada_query()
|
|
# ----------------------
|
|
# Query ClearSCADA raw historical files using the ClearSCADA command line tool to create
|
|
# csv data from the raw data files, then process and merge the data into VTScada formats
|
|
|
|
|
|
def clearscada_query(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
|
|
dir_path = output_path + str(start_time.year) + "\\"
|
|
create_directory(dir_path)
|
|
|
|
current_start_time = start_time
|
|
current_end_time = end_time
|
|
|
|
start_week = weeks_since_date(current_start_time.timestamp())
|
|
end_week = weeks_since_date(current_end_time.timestamp())
|
|
|
|
historic_directories = []
|
|
tags = []
|
|
|
|
# Get a list of all directories of Historic files (format is Historic ID with ID padded with leading zeroes) needed which exist
|
|
for tag in historical_tags:
|
|
# For ClearSCADA, the tag source is the ID code
|
|
padded_id = f'{int(tag.name_source):06}'
|
|
|
|
# Check that directory exists and if so, add it to a list
|
|
path = os.path.join(historic_files, "Historic " + padded_id)
|
|
if os.path.exists(path):
|
|
historic_directories.append(path)
|
|
tags.append(tag)
|
|
|
|
zipped_directories = zip(historic_directories, tags)
|
|
|
|
# For each found historic directory execute the ClearSCADA CSV command
|
|
tag_mappings = []
|
|
|
|
for (path, tag) in zipped_directories:
|
|
# print(path, tag.name_dest)
|
|
|
|
command = os.path.join(install_location, "SCXCMD.exe")
|
|
|
|
for file in os.listdir(path):
|
|
if os.fsdecode(file).endswith(".HRD"):
|
|
week_number = int(file[2:8])
|
|
if week_number >= start_week and week_number <= end_week:
|
|
argument = os.path.join(path, file)
|
|
subprocess.run([command, "HISDUMP", argument])
|
|
|
|
# Process each directory of CSVs first into a list of values that can be pruned
|
|
values = []
|
|
output_file = ""
|
|
|
|
for file in os.listdir(path):
|
|
if os.fsdecode(file).endswith(".csv"):
|
|
csv_file = os.path.join(path, file)
|
|
|
|
values.extend(read_clearscada_file(csv_file))
|
|
|
|
# Values will have had their deadband and scaling processed, but remaining is excess frequency
|
|
if len(values) > 0:
|
|
values = postprocess_values(values)
|
|
output_file = prepare_file_for_tag(
|
|
tag, values, dir_path, current_end_time, True)
|
|
tag_mappings.append((output_file, tag.name_dest))
|
|
|
|
write_tagmapping_to_file(
|
|
dir_path + "TagMapping.csv", tag_mappings)
|
|
|
|
# main_directory = os.fsencode(historic_files)
|
|
|
|
|
|
# clearscada_read_file()
|
|
# ----------------------
|
|
# Read in a ClearSCADA CSV file converted from HRD into a list of timestamps and values
|
|
def clearscada_read_file(file_path: str) -> List[Union[int, float, None]]:
|
|
values = []
|
|
|
|
with open(file_path, mode='r', encoding='utf-8-sig') as csvfile:
|
|
csv_reader = csv.reader(csvfile, delimiter=',')
|
|
next(csv_reader) # Skip the header row
|
|
|
|
for row, line in enumerate(csv_reader):
|
|
if line[2] == "Good":
|
|
timestamp = datetime.timestamp(
|
|
datetime.strptime(line[0], "%d/%m/%Y %H:%M:%S"))
|
|
value = float(line[1])
|
|
values.append((timestamp, value))
|
|
|
|
return values
|
|
|
|
|
|
# compress_and_scale_real()
|
|
# ----------------------
|
|
# -- Deadband (only keeping values which change by the required amount)
|
|
# -- Precision (decimal places, cleaning up excess data from floating points)
|
|
# -- Scaling factor (applies the scaling factor to the value before assigning the precision)
|
|
|
|
def compress_and_scale_real(values: List[Union[int, float, None]], deadband: float, scale_factor: float, precision: int) -> List[Union[int, float, None]]:
|
|
compressed_values = []
|
|
working_value = None
|
|
|
|
for value_pair in values:
|
|
timestamp, value = value_pair
|
|
|
|
if value is None:
|
|
continue
|
|
|
|
if working_value is None or abs(value - working_value) > deadband:
|
|
working_value = value
|
|
scaled_value = round(value * scale_factor, precision)
|
|
compressed_values.append((timestamp, scaled_value))
|
|
|
|
return compressed_values
|
|
|
|
# compress_boolean()
|
|
# ----------------------
|
|
# Compress a set of timestamp and boolean values to transitions. For booleans, transitions are
|
|
# kept and the assumption is
|
|
# the interval will be fast enough to keep all transitions.
|
|
|
|
|
|
def compress_boolean(values: List[Union[int, float, None]]) -> List[Union[int, float, None]]:
|
|
compressed_values = []
|
|
working_value = None
|
|
|
|
for value_pair in values:
|
|
timestamp, value = value_pair
|
|
|
|
if value is None:
|
|
continue
|
|
|
|
if working_value is None or value != working_value:
|
|
working_value = value
|
|
compressed_values.append((timestamp, value))
|
|
|
|
return compressed_values
|
|
|
|
# create_directory()
|
|
# ----------------------
|
|
# Create a directory if it doesn't exist
|
|
|
|
|
|
def create_directory(path):
|
|
if not os.path.exists(path):
|
|
os.makedirs(path)
|
|
|
|
# postprocess_values()
|
|
# ----------------------
|
|
# Process a list of values assumed and clean up timestamps which are within the interval of the last
|
|
# timestamp. Values are assumed to already have been compressed
|
|
|
|
|
|
def postprocess_values(values: List[Union[int, float, None]]):
|
|
|
|
last_time = time.time()
|
|
|
|
processed_values = []
|
|
|
|
for (timestamp, value) in values:
|
|
timedelta = abs(last_time - timestamp)
|
|
|
|
if timedelta > 50:
|
|
processed_values.append((timestamp, value))
|
|
last_time = timestamp
|
|
|
|
last_time = timestamp
|
|
|
|
return processed_values
|
|
|
|
# prepare_file_for_tag()
|
|
# ----------------------
|
|
|
|
|
|
def prepare_file_for_tag(tag: HistoricalTag, values: List[Union[int, float, None]], dir_path: str, current_end_time: datetime, append=False) -> str:
|
|
if values is None:
|
|
print("No values found")
|
|
return ""
|
|
else:
|
|
output_file = ""
|
|
|
|
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)
|
|
|
|
if len(compressed_values) != 0:
|
|
output_file = tag.name_source.replace('\\', '_') + "_" + str(current_end_time.year) + str(
|
|
current_end_time.month) + str(current_end_time.day) + ".csv"
|
|
full_output_file = dir_path + output_file
|
|
write_values_to_file(full_output_file, compressed_values, True)
|
|
|
|
return output_file
|
|
|
|
# print_text()
|
|
# ----------------------
|
|
# Print formatting a text line for debugging and such
|
|
|
|
|
|
def print_text(text: str):
|
|
print(r'-------------------------------------------------------------------------------------------------------')
|
|
print(text)
|
|
print(r'-------------------------------------------------------------------------------------------------------')
|
|
|
|
# read_tags()
|
|
# ----------------------
|
|
# Read in the list of tags and set the mapping parameters for each tag and construct the groupings required for the
|
|
# query
|
|
|
|
|
|
def read_tags(file_path: str) -> List[HistoricalTag]:
|
|
historical_tags = []
|
|
|
|
with open(file_path, mode='r', encoding='utf-8-sig') as csvfile:
|
|
csv_reader = csv.reader(csvfile, delimiter=',')
|
|
next(csv_reader) # Skip the header row
|
|
|
|
for row, line in enumerate(csv_reader):
|
|
name_source, name_dest, tag_type, scale_factor, interval, precision, deadband = line
|
|
tag = HistoricalTag(row=row+1, tag_type=tag_type, name_source=name_source, name_dest=name_dest,
|
|
scale_factor=float(scale_factor), interval=int(interval), precision=int(precision), deadband=float(deadband))
|
|
historical_tags.append(tag)
|
|
|
|
return historical_tags
|
|
|
|
# vtscada_tag_query()
|
|
# ----------------------
|
|
# Given a HistoricalTag structure, query the tag's values from the start time to the end time
|
|
|
|
|
|
def vtscada_tag_query(historical_tag: HistoricalTag, ft_start_time: datetime, ft_end_time: datetime) -> List[Union[int, float, None]]:
|
|
# Query average only for real values (Analog in VTScada)
|
|
if historical_tag.tag_type == "real":
|
|
value_string = ":Value:Average"
|
|
# Otherwise, query the value at the start of the interval
|
|
else:
|
|
value_string = ":Value:ValueAtStart"
|
|
|
|
query = "SELECT Timestamp, '" + historical_tag.name_source + value_string + "' FROM History_" + \
|
|
str(historical_tag.interval) + "s" + " WHERE Timestamp BETWEEN " + \
|
|
ft_start_time + " AND " + ft_end_time
|
|
|
|
url = "http://" + server + ":" + realm_port + \
|
|
"/" + realm_name + "/REST/SQLQuery?query=" + query
|
|
|
|
# print_text(url)
|
|
|
|
response = requests.get(url, auth=(application_user, application_pass))
|
|
returned = response.json()
|
|
|
|
return returned['results']['values']
|
|
|
|
# vtscada_query()
|
|
# ----------------------
|
|
# Given the set of HistoricalTags and a start and end time, query the data of those tags from the
|
|
# REST interface
|
|
|
|
|
|
def vtscada_query(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
|
|
current_start_time = start_time
|
|
current_end_time = start_time + timedelta(days=1)
|
|
|
|
while current_start_time < end_time:
|
|
print("Querying data for: " + str(current_start_time.year) + " " +
|
|
str(current_start_time.month) + " " + str(current_start_time.day))
|
|
dir_path = output_path + str(start_time.year) + "\\"
|
|
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:
|
|
values = vtscada_tag_query(tag, ft_start_time, ft_end_time)
|
|
output_file = prepare_file_for_tag(
|
|
tag, values, dir_path, current_end_time)
|
|
|
|
if output_file != "":
|
|
tag_mappings.append((output_file, tag.name_dest))
|
|
|
|
write_tagmapping_to_file(
|
|
dir_path + "TagMapping.csv", tag_mappings)
|
|
|
|
current_start_time += timedelta(days=1)
|
|
current_end_time += timedelta(days=1)
|
|
|
|
|
|
# write_tagmappings_to_file()
|
|
# ----------------------
|
|
# Create a new TagMapping.CSV file which contains the mapping of all tag names and files which
|
|
# contain their CSV data
|
|
|
|
|
|
def write_tagmapping_to_file(output_file: str, tag_mappings: List[str]):
|
|
with open(output_file, mode='a', newline='', encoding='utf-8') as csvfile:
|
|
csv_writer = csv.writer(csvfile)
|
|
|
|
for mapping in tag_mappings:
|
|
csv_writer.writerow(mapping)
|
|
|
|
# write_values_to_file()
|
|
# ----------------------
|
|
# Given a full path name of a file and list of timestamp, value pairs, write the values to a
|
|
# CSV file with each pair on its own row.
|
|
|
|
|
|
def write_values_to_file(output_file: str, values: List[Union[int, float, None]], append=False):
|
|
|
|
if append:
|
|
csv_mode = 'a'
|
|
else:
|
|
csv_mode = 'w'
|
|
|
|
with open(output_file, mode=csv_mode, newline='', encoding='utf-8') as csvfile:
|
|
csv_writer = csv.writer(csvfile)
|
|
|
|
for value_pair in values:
|
|
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])
|
|
|
|
|
|
# weeks_since_date()
|
|
# ----------------------
|
|
# Returns the number of weeks since the given timestamp, or defaults to December 25th, 1600
|
|
|
|
|
|
def weeks_since_date(timestamp, date=(1600, 12, 25)):
|
|
dt = datetime.utcfromtimestamp(timestamp)
|
|
start_date = datetime(*date)
|
|
delta = dt - start_date
|
|
weeks = delta.days // 7
|
|
|
|
return weeks
|
|
|
|
|
|
# ----------------------
|
|
# Main Section
|
|
# ----------------------
|
|
|
|
|
|
print(r' _ _ _____ _____ _______ ____ _____ _____ _____ _ _______ ____ ____ _ _____ ')
|
|
print(r'| | | |_ _|/ ____|__ __/ __ \| __ \|_ _/ ____| /\ | | |__ __/ __ \ / __ \| | / ____|')
|
|
print(r'| |__| | | | | (___ | | | | | | |__) | | || | / \ | | | | | | | | | | | | | (___ ')
|
|
print(r'| __ | | | \___ \ | | | | | | _ / | || | / /\ \ | | | | | | | | | | | | \___ \ ')
|
|
print(r'| | | |_| |_ ____) | | | | |__| | | \ \ _| || |____ / ____ \| |____ | | | |__| | |__| | |____ ____) |')
|
|
print(r'|_| |_|_____|_____/ |_| \____/|_| \_\_____\_____/_/ \_\______| |_| \____/ \____/|______|_____/ ')
|
|
|
|
config = toml.load("setup.toml")
|
|
|
|
tags_path = config['system']['tags_path']
|
|
output_path = config['system']['output_path']
|
|
system_timezone = config['system']['system_timezone']
|
|
application_user = config['user']['application_user']
|
|
application_pass = config['user']['application_pass']
|
|
|
|
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)
|
|
|
|
for tag in historical_tags:
|
|
print(tag)
|
|
|
|
if query_type == "VTScada":
|
|
print_text('VTScada Data Query')
|
|
|
|
server = config['vtscada']['server_name']
|
|
realm_port = config['vtscada']['realm_port']
|
|
realm_name = config['vtscada']['realm_name']
|
|
|
|
vtscada_query(historical_tags, start_time, end_time)
|
|
elif query_type == "AVEVA":
|
|
print_text('AVEVA Historian - Not Implemented')
|
|
elif query_type == "ClearSCADA":
|
|
print_text('ClearSCADA - Query Raw Historic Files')
|
|
historic_files = config['clearscada']['historic_files']
|
|
install_location = config['clearscada']['install_location']
|
|
delete_processed = config['clearscada']['delete_processed']
|
|
|
|
clearscada_generate_historical_ids(historic_files)
|
|
clearscada_query(historical_tags, start_time, end_time)
|
|
|
|
else:
|
|
print("Invalid arguments!")
|