Adding ClearSCADA raw data functions.
This commit is contained in:
parent
8dbb030dd9
commit
06a6d5aa88
@ -1,2 +1,3 @@
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# StandardTemplate
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# VTScada-HistoricalTools
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Key Point: In the tags list file, the Source Name field is the unique identifier for the tag name to query. In VTScada this can be something like ```temp\old_value1```. In ClearSCADA, it will be the unique point ID, ex. ```005152```. The leading zeroes can be left out as the script will pad them in front of the integere to determine the correct path.
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@ -1,17 +1,17 @@
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Source Name,Destination Name,Data Type,Scale Factor,Interval (s),Precision,Deadband
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temp\old_value1,temp\new_value1,real,1,20,2,0
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temp\old_value2,temp\new_value2,integer,10,100,0,0
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temp\old_value3,temp\new_value3,real,1.5,100,2,0
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temp\old_value4,temp\new_value4,boolean,1,9,0,0
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temp\old_value5,temp\new_value5,real,1,20,2,0
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temp\old_value6,temp\new_value6,integer,10,100,0,0
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temp\old_value7,temp\new_value7,real,1.5,100,2,0
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temp\old_value8,temp\new_value8,boolean,1,9,0,0
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temp\old_value9,temp\new_value9,real,1,10,2,0
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temp\old_value10,temp\new_value10,integer,1,10,0,0
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temp\old_value11,temp\new_value11,real,1,10,4,0
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temp\old_value12,temp\new_value12,boolean,1,10,0,0
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temp\old_value13,temp\new_value13,real,1,12,4,0
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temp\old_value14,temp\new_value14,integer,1,13,0,0
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temp\old_value15,temp\new_value15,real,1,14,4,0
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temp\old_value16,temp\new_value16,boolean,1,15,0,0
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11959,temp\new_value1,real,1,20,2,0.01
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5153,temp\new_value2,integer,1,100,0,0
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5154,temp\new_value3,real,1,30,0,1
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5155,temp\new_value4,boolean,1,9,0,0
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5,temp\new_value5,real,1,20,2,0
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6,temp\new_value6,integer,10,100,0,0
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227,temp\new_value7,real,1.5,100,2,0
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8,temp\new_value8,boolean,1,9,0,0
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9,temp\new_value9,real,1,10,2,0
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10,temp\new_value10,integer,1,10,0,0
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011,temp\new_value11,real,1,10,4,0
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12,temp\new_value12,boolean,1,10,0,0
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1113,temp\new_value13,real,1,12,4,0
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14,temp\new_value14,integer,1,13,0,0
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1665,temp\new_value15,real,1,14,4,0
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16,temp\new_value16,boolean,1,15,0,0
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main.py
301
main.py
@ -3,6 +3,9 @@ import toml
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import sys
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import os
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import requests
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import pyodbc
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import subprocess
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import time
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from typing import List, Union
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from datetime import datetime, timezone, timedelta
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@ -38,13 +41,127 @@ class HistoricalTag:
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def __repr__(self):
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return f"({self.row}, {self.tag_type}, {self.name_source}, {self.name_dest}, {self.scale_factor}, {self.interval}, {self.precision}, {self.deadband})"
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# ----------------------
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# Functions
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# ----------------------
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# clearscada_generate_historical_ids()
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# ----------------------
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# Generates a list of historical IDs for found historic files
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def clearscada_generate_historical_ids(historic_files: str):
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ids = []
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for directory in os.listdir(historic_files):
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if os.fsdecode(directory).startswith("Historic "):
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ids.append(int(directory[9:15]))
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output_file = os.path.join(output_path, "CS_HistoricIDs.CSV")
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with open(output_file, mode='w', newline='', encoding='utf-8') as csvfile:
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csv_writer = csv.writer(csvfile)
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for id in ids:
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if id is not None:
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csv_writer.writerow([str(id)])
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# clearscada_query()
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# ----------------------
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# Query ClearSCADA raw historical files using the ClearSCADA command line tool to create
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# csv data from the raw data files, then process and merge the data into VTScada formats
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def clearscada_query(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
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dir_path = output_path + str(start_time.year) + "\\"
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create_directory(dir_path)
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current_start_time = start_time
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current_end_time = end_time
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start_week = weeks_since_date(current_start_time.timestamp())
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end_week = weeks_since_date(current_end_time.timestamp())
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historic_directories = []
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tags = []
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# Get a list of all directories of Historic files (format is Historic ID with ID padded with leading zeroes) needed which exist
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for tag in historical_tags:
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# For ClearSCADA, the tag source is the ID code
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padded_id = f'{int(tag.name_source):06}'
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# Check that directory exists and if so, add it to a list
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path = os.path.join(historic_files, "Historic " + padded_id)
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if os.path.exists(path):
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historic_directories.append(path)
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tags.append(tag)
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zipped_directories = zip(historic_directories, tags)
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# For each found historic directory execute the ClearSCADA CSV command
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tag_mappings = []
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for (path, tag) in zipped_directories:
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# print(path, tag.name_dest)
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command = os.path.join(install_location, "SCXCMD.exe")
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for file in os.listdir(path):
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if os.fsdecode(file).endswith(".HRD"):
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week_number = int(file[2:8])
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if week_number >= start_week and week_number <= end_week:
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argument = os.path.join(path, file)
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subprocess.run([command, "HISDUMP", argument])
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# Process each directory of CSVs first into a list of values that can be pruned
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values = []
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output_file = ""
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for file in os.listdir(path):
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if os.fsdecode(file).endswith(".csv"):
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csv_file = os.path.join(path, file)
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values.extend(read_clearscada_file(csv_file))
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# Values will have had their deadband and scaling processed, but remaining is excess frequency
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if len(values) > 0:
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values = postprocess_values(values)
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output_file = prepare_file_for_tag(
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tag, values, dir_path, current_end_time, True)
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tag_mappings.append((output_file, tag.name_dest))
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write_tagmapping_to_file(
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dir_path + "TagMapping.csv", tag_mappings)
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# main_directory = os.fsencode(historic_files)
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# clearscada_read_file()
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# ----------------------
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# Read in a ClearSCADA CSV file converted from HRD into a list of timestamps and values
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def clearscada_read_file(file_path: str) -> List[Union[int, float, None]]:
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values = []
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with open(file_path, mode='r', encoding='utf-8-sig') as csvfile:
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csv_reader = csv.reader(csvfile, delimiter=',')
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next(csv_reader) # Skip the header row
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for row, line in enumerate(csv_reader):
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if line[2] == "Good":
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timestamp = datetime.timestamp(
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datetime.strptime(line[0], "%d/%m/%Y %H:%M:%S"))
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value = float(line[1])
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values.append((timestamp, value))
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return values
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# compress_and_scale_real()
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# ----------------------
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# -- Deadband (only keeping values which change by the required amount)
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# -- Precision (decimal places, cleaning up excess data from floating points)
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# -- Scaling factor (applies the scaling factor to the value before assigning the precision)
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def compress_and_scale_real(values: List[Union[int, float, None]], deadband: float, scale_factor: float, precision: int) -> List[Union[int, float, None]]:
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compressed_values = []
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working_value = None
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@ -64,6 +181,9 @@ def compress_and_scale_real(values: List[Union[int, float, None]], deadband: flo
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# compress_boolean()
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# ----------------------
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# Compress a set of timestamp and boolean values to transitions. For booleans, transitions are
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# kept and the assumption is
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# the interval will be fast enough to keep all transitions.
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def compress_boolean(values: List[Union[int, float, None]]) -> List[Union[int, float, None]]:
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@ -84,14 +204,64 @@ def compress_boolean(values: List[Union[int, float, None]]) -> List[Union[int, f
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# create_directory()
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# ----------------------
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# Create a directory if it doesn't exist
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def create_directory(path):
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if not os.path.exists(path):
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os.makedirs(path)
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# postprocess_values()
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# ----------------------
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# Process a list of values assumed and clean up timestamps which are within the interval of the last
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# timestamp. Values are assumed to already have been compressed
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def postprocess_values(values: List[Union[int, float, None]]):
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last_time = time.time()
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processed_values = []
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for (timestamp, value) in values:
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timedelta = abs(last_time - timestamp)
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if timedelta > 50:
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processed_values.append((timestamp, value))
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last_time = timestamp
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last_time = timestamp
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return processed_values
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# prepare_file_for_tag()
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# ----------------------
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def prepare_file_for_tag(tag: HistoricalTag, values: List[Union[int, float, None]], dir_path: str, current_end_time: datetime, append=False) -> str:
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if values is None:
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print("No values found")
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return ""
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else:
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output_file = ""
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if tag.tag_type == "real" or tag.tag_type == "integer":
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compressed_values = compress_and_scale_real(
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values, tag.deadband, tag.scale_factor, tag.precision)
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else:
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compressed_values = compress_boolean(values)
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if len(compressed_values) != 0:
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output_file = tag.name_source.replace('\\', '_') + "_" + str(current_end_time.year) + str(
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current_end_time.month) + str(current_end_time.day) + ".csv"
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full_output_file = dir_path + output_file
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write_values_to_file(full_output_file, compressed_values, True)
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return output_file
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# print_text()
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# ----------------------
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# Print formatting a text line for debugging and such
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def print_text(text: str):
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print(text)
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print(r'-------------------------------------------------------------------------------------------------------')
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# query_vtscada_tag()
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# read_tags()
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# ----------------------
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# Read in the list of tags and set the mapping parameters for each tag and construct the groupings required for the
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# query
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def query_vtscada_tag(historical_tag: HistoricalTag, ft_start_time: datetime, ft_end_time: datetime) -> List[Union[int, float, None]]:
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def read_tags(file_path: str) -> List[HistoricalTag]:
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historical_tags = []
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with open(file_path, mode='r', encoding='utf-8-sig') as csvfile:
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csv_reader = csv.reader(csvfile, delimiter=',')
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next(csv_reader) # Skip the header row
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for row, line in enumerate(csv_reader):
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name_source, name_dest, tag_type, scale_factor, interval, precision, deadband = line
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tag = HistoricalTag(row=row+1, tag_type=tag_type, name_source=name_source, name_dest=name_dest,
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scale_factor=float(scale_factor), interval=int(interval), precision=int(precision), deadband=float(deadband))
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historical_tags.append(tag)
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return historical_tags
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# vtscada_tag_query()
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# ----------------------
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# Given a HistoricalTag structure, query the tag's values from the start time to the end time
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def vtscada_tag_query(historical_tag: HistoricalTag, ft_start_time: datetime, ft_end_time: datetime) -> List[Union[int, float, None]]:
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# Query average only for real values (Analog in VTScada)
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if historical_tag.tag_type == "real":
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value_string = ":Value:Average"
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@ -125,11 +317,13 @@ def query_vtscada_tag(historical_tag: HistoricalTag, ft_start_time: datetime, ft
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return returned['results']['values']
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# query_vtscada()
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# vtscada_query()
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# ----------------------
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# Given the set of HistoricalTags and a start and end time, query the data of those tags from the
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# REST interface
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def query_vtscada(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
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def vtscada_query(historical_tags: List[HistoricalTag], start_time: datetime, end_time: datetime):
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current_start_time = start_time
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current_end_time = start_time + timedelta(days=1)
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@ -147,7 +341,7 @@ def query_vtscada(historical_tags: List[HistoricalTag], start_time: datetime, en
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tag_mappings = []
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for tag in historical_tags:
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values = query_vtscada_tag(tag, ft_start_time, ft_end_time)
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values = vtscada_tag_query(tag, ft_start_time, ft_end_time)
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output_file = prepare_file_for_tag(
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tag, values, dir_path, current_end_time)
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@ -160,37 +354,34 @@ def query_vtscada(historical_tags: List[HistoricalTag], start_time: datetime, en
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current_start_time += timedelta(days=1)
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current_end_time += timedelta(days=1)
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# prepare_file_for_tag()
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# write_tagmappings_to_file()
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# ----------------------
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# Create a new TagMapping.CSV file which contains the mapping of all tag names and files which
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# contain their CSV data
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def prepare_file_for_tag(tag: HistoricalTag, values: List[Union[int, float, None]], dir_path: str, current_end_time: datetime) -> str:
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if values is None:
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print("No values found")
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return ""
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else:
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output_file = ""
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def write_tagmapping_to_file(output_file: str, tag_mappings: List[str]):
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with open(output_file, mode='a', newline='', encoding='utf-8') as csvfile:
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csv_writer = csv.writer(csvfile)
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if tag.tag_type == "real" or tag.tag_type == "integer":
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compressed_values = compress_and_scale_real(
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values, tag.deadband, tag.scale_factor, tag.precision)
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else:
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compressed_values = compress_boolean(values)
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if len(compressed_values) != 0:
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output_file = tag.name_source.replace('\\', '_') + "_" + str(current_end_time.year) + str(
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current_end_time.month) + str(current_end_time.day) + ".csv"
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full_output_file = dir_path + output_file
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write_values_to_file(full_output_file, compressed_values)
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return output_file
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for mapping in tag_mappings:
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csv_writer.writerow(mapping)
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# write_values_to_file()
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# ----------------------
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# Given a full path name of a file and list of timestamp, value pairs, write the values to a
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# CSV file with each pair on its own row.
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def write_values_to_file(output_file: str, values: List[Union[int, float, None]]):
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with open(output_file, mode='w', newline='', encoding='utf-8') as csvfile:
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def write_values_to_file(output_file: str, values: List[Union[int, float, None]], append=False):
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if append:
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csv_mode = 'a'
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else:
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csv_mode = 'w'
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with open(output_file, mode=csv_mode, newline='', encoding='utf-8') as csvfile:
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csv_writer = csv.writer(csvfile)
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for value_pair in values:
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@ -201,37 +392,21 @@ def write_values_to_file(output_file: str, values: List[Union[int, float, None]]
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'%Y-%m-%d %H:%M:%S.%f')[:-3]
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csv_writer.writerow([formatted_timestamp, value])
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# write_tagmappings_to_file()
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# weeks_since_date()
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# ----------------------
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# Returns the number of weeks since the given timestamp, or defaults to December 25th, 1600
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def write_tagmapping_to_file(output_file: str, tag_mappings: List[str]):
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with open(output_file, mode='a', newline='', encoding='utf-8') as csvfile:
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csv_writer = csv.writer(csvfile)
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def weeks_since_date(timestamp, date=(1600, 12, 25)):
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dt = datetime.utcfromtimestamp(timestamp)
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start_date = datetime(*date)
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delta = dt - start_date
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weeks = delta.days // 7
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for mapping in tag_mappings:
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csv_writer.writerow(mapping)
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return weeks
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# read_tags()
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# ----------------------
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# Read in the list of tags and set the mapping parameters for each tag and construct the groupings required for the
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# query
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def read_tags(file_path: str) -> List[HistoricalTag]:
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historical_tags = []
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with open(file_path, mode='r', encoding='utf-8-sig') as csvfile:
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csv_reader = csv.reader(csvfile, delimiter=',')
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next(csv_reader) # Skip the header row
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for row, line in enumerate(csv_reader):
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name_source, name_dest, tag_type, scale_factor, interval, precision, deadband = line
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tag = HistoricalTag(row=row+1, tag_type=tag_type, name_source=name_source, name_dest=name_dest,
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scale_factor=float(scale_factor), interval=int(interval), precision=int(precision), deadband=float(deadband))
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historical_tags.append(tag)
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return historical_tags
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# ----------------------
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# Main Section
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# ----------------------
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@ -252,10 +427,6 @@ system_timezone = config['system']['system_timezone']
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application_user = config['user']['application_user']
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application_pass = config['user']['application_pass']
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server = config['vtscada']['server_name']
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realm_port = config['vtscada']['realm_port']
|
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realm_name = config['vtscada']['realm_name']
|
||||
|
||||
if len(sys.argv) == 4:
|
||||
query_type = sys.argv[1]
|
||||
|
||||
@ -273,10 +444,22 @@ if len(sys.argv) == 4:
|
||||
|
||||
if query_type == "VTScada":
|
||||
print_text('VTScada Data Query')
|
||||
query_vtscada(historical_tags, start_time, end_time)
|
||||
|
||||
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 - Not Implemented')
|
||||
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!")
|
||||
|
||||
@ -12,6 +12,12 @@ server_name = "lb-vanryn"
|
||||
realm_port = "8888"
|
||||
realm_name = "RESTRealm"
|
||||
|
||||
[clearscada]
|
||||
historic_files = "C:\\ProgramData\\Schneider Electric\\ClearSCADA\\Database\\HisFiles"
|
||||
install_location = "C:\\Program Files (x86)\\Schneider Electric\\ClearSCADA"
|
||||
# Set true to clear out the Historic files as they are processed
|
||||
delete_processed = false
|
||||
|
||||
[user]
|
||||
application_user = "query"
|
||||
application_pass = "queryuser"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user