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firmware/opt/innovenergy/scripts/extractS3data.py
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import os
import csv
import subprocess
import argparse
import matplotlib.pyplot as plt
from collections import defaultdict
def extract_timestamp(filename):
timestamp_str = filename[:10]
try:
timestamp = int(timestamp_str)
return timestamp
except ValueError:
return 0
def extract_values_by_key(csv_file, key, exact_match):
# Initialize a defaultdict for lists
matched_values = defaultdict(list)
with open(csv_file, 'r') as file:
reader = csv.reader(file)
for row in reader:
if row:
columns = row[0].split(';')
if len(columns) > 1:
first_column = columns[0].strip()
path_key = first_column.split('/')[-1]
for key_item in key:
if exact_match:
if key_item.lower() == row[0].split('/')[-1].split(';')[0].lower():
matched_values[path_key].append(row[0])
else:
if key_item.lower() in first_column.lower():
matched_values[path_key].append(row[0])
#return matched_values
# Concatenate all keys to create a single final_key
final_key = ''.join(matched_values.keys())
# Combine all lists of values into a single list
combined_values = []
for values in matched_values.values():
combined_values.extend(values)
# Create the final dictionary with final_key and all combined values
final_dict = {final_key: combined_values}
#return dict(matched_values)
return final_dict
def list_files_in_range(start_timestamp, end_timestamp, sampling_stepsize):
filenames_in_range = [f"{timestamp:10d}" for timestamp in range(start_timestamp, end_timestamp + 1, 2*sampling_stepsize)]
return filenames_in_range
def check_s3_files_exist(bucket_number, filename):
s3cmd_ls_command = f"s3cmd ls s3://{bucket_number}-3e5b3069-214a-43ee-8d85-57d72000c19d/{filename}*"
try:
result = subprocess.run(s3cmd_ls_command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
lines = result.stdout.decode().split('\n')[:-1]
filenames = [line.split()[-1].split('/')[-1] for line in lines]
return filenames
except subprocess.CalledProcessError as e:
print(f"Error checking S3 files: {e}")
return []
def download_files(bucket_number, filenames_to_download):
output_directory = f"S3cmdData_{bucket_number}"
if not os.path.exists(output_directory):
os.makedirs(output_directory)
print(f"Directory '{output_directory}' created.")
for filename in filenames_to_download:
stripfilename = filename.strip()
local_path = os.path.join(output_directory, stripfilename + ".csv")
if not os.path.exists(local_path):
s3cmd_command = f"s3cmd get s3://{bucket_number}-3e5b3069-214a-43ee-8d85-57d72000c19d/{stripfilename}.csv {output_directory}/"
try:
subprocess.run(s3cmd_command, shell=True, check=True)
downloaded_files = [file for file in os.listdir(output_directory) if file.startswith(filename)]
if not downloaded_files:
print(f"No matching files found for prefix '{filename}'.")
else:
print(f"Files with prefix '{filename}' downloaded successfully.")
except subprocess.CalledProcessError as e:
print(f"Error downloading files: {e}")
continue
else:
print(f"File '{filename}.csv' already exists locally. Skipping download.")
def visualize_data(data, output_directory):
# Extract data for visualization (replace this with your actual data extraction)
x_values = [int(entry[0]) for entry in data]
y_values = [float(entry[1]) for entry in data]
# Plotting
plt.plot(x_values, y_values, marker='o', linestyle='-', color='b')
plt.xlabel('Timestamp')
plt.ylabel('Your Y-axis Label')
plt.title('Your Plot Title')
plt.grid(True)
plt.savefig(os.path.join(output_directory, f"{start_timestamp}_{key}_plot.png"))
plt.close() # Close the plot window
# Save data to CSV
csv_file_path = os.path.join(output_directory, f"{start_timestamp}_{key}_extracted.csv")
with open(csv_file_path, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile)
csv_writer.writerow(['Timestamp', 'Value']) # Adjust column names as needed
csv_writer.writerows(data)
def get_last_component(path):
path_without_slashes = path.replace('/', '')
return path_without_slashes
def download_and_process_files(bucket_number, start_timestamp, end_timestamp, sampling_stepsize, key, booleans_as_numbers, exact_match):
output_directory = f"S3cmdData_{bucket_number}"
if not os.path.exists(output_directory):
os.makedirs(output_directory)
print(f"Directory '{output_directory}' created.")
filenames_to_check = list_files_in_range(start_timestamp, end_timestamp, sampling_stepsize)
#filenames_on_s3 = check_s3_files_exist(bucket_number, filenames_to_check, key)
existing_files = [filename for filename in filenames_to_check if os.path.exists(os.path.join(output_directory, f"{filename}.csv"))]
files_to_download = set(filenames_to_check) - set(existing_files)
if os.listdir(output_directory):
print("Files already exist in the local folder. Skipping download.")
else:
if files_to_download:
download_files(bucket_number, files_to_download)
# Process CSV files
csv_files = [file for file in os.listdir(output_directory) if file.endswith('.csv')]
csv_files.sort(key=extract_timestamp)
keypath = ''
for key_item in key:
keypath+= get_last_component(key_item)
output_csv_filename = f"{keypath}_{start_timestamp}_{bucket_number}.csv"
with open(output_csv_filename, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile)
header = ['time']
add_header = True
for csv_file in csv_files:
file_path = os.path.join(output_directory, csv_file)
extracted_values = extract_values_by_key(file_path, key, exact_match)
if add_header:
add_header = False
for values in extracted_values.values():
first_value = values
for first_val in first_value:
header.append(first_val.split(';')[0].strip())
break
csv_writer.writerow(header)
if extracted_values:
for first_column, values in extracted_values.items():
if booleans_as_numbers:
values = [1 if value.split(';')[1].strip() == "True" else 0 if value.split(';')[1].strip() == "False" else value.split(';')[1].strip() for value in values]
values_list = []
values_list.append(csv_file.replace(".csv", ""))
for i, value in enumerate(values):
if value is None:
value = "No value provided"
else:
values_list.append(value.split(';')[1].strip())
csv_writer.writerow(values_list)
print(f"Extracted data saved in '{output_csv_filename}'.")
def parse_keys(input_string):
# Split the input string by commas and strip whitespace
keys = [key.strip() for key in input_string.split(',')]
# Return keys as a list if more than one, else return the single key
#return keys if len(keys) > 1 else keys[0]
return keys
def main():
parser = argparse.ArgumentParser(description='Download files from S3 using s3cmd and extract specific values from CSV files.')
parser.add_argument('start_timestamp', type=int, help='The start timestamp for the range (even number)')
parser.add_argument('end_timestamp', type=int, help='The end timestamp for the range (even number)')
#parser.add_argument('--key', type=str, required=True, help='The part to match from each CSV file')
parser.add_argument('--keys', type=parse_keys, required=True, help='The part to match from each CSV file, can be a single key or a comma-separated list of keys')
parser.add_argument('--bucket-number', type=int, required=True, help='The number of the bucket to download from')
parser.add_argument('--sampling_stepsize', type=int, required=False, default=1, help='The number of 2sec intervals, which define the length of the sampling interval in S3 file retrieval')
parser.add_argument('--booleans_as_numbers', action="store_true", required=False, help='If key used, then booleans are converted to numbers [0/1], if key not used, then booleans maintained as text [False/True]')
parser.add_argument('--exact_match', action="store_true", required=False, help='If key used, then key has to match exactly "=", else it is enough that key is found "in" text')
args = parser.parse_args();
start_timestamp = args.start_timestamp
end_timestamp = args.end_timestamp
keys = args.keys
bucket_number = args.bucket_number
sampling_stepsize = args.sampling_stepsize
booleans_as_numbers = args.booleans_as_numbers
exact_match = args.exact_match
# Check if start_timestamp is smaller than end_timestamp
if start_timestamp >= end_timestamp:
print("Error: start_timestamp must be smaller than end_timestamp.")
return
download_and_process_files(bucket_number, start_timestamp, end_timestamp, sampling_stepsize, keys, booleans_as_numbers, exact_match)
if __name__ == "__main__":
main()