extarct s3 data with decompression

firmware/opt/innovenergy/scripts/extractS3data.py

@@ -4,6 +4,9 @@ import subprocess
 import argparse
 import matplotlib.pyplot as plt
 from collections import defaultdict
+import zipfile
+import base64
+import shutil
 
 def extract_timestamp(filename):
     timestamp_str = filename[:10]
@@ -14,7 +17,6 @@ def extract_timestamp(filename):
         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)
@@ -31,37 +33,26 @@ def extract_values_by_key(csv_file, key, exact_match):
                         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):
+def download_files(bucket_number, filenames_to_download, product_type):
-    s3cmd_ls_command = f"s3cmd ls s3://{bucket_number}-3e5b3069-214a-43ee-8d85-57d72000c19d/{filename}*"
+    if product_type == 0:
-    try:
+        hash = "3e5b3069-214a-43ee-8d85-57d72000c19d"
-        result = subprocess.run(s3cmd_ls_command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    elif product_type == 1:
-        lines = result.stdout.decode().split('\n')[:-1]
+        hash = "c0436b6a-d276-4cd8-9c44-1eae86cf5d0e"
-        filenames = [line.split()[-1].split('/')[-1] for line in lines]
+    else:
-        return filenames
+        raise ValueError("Invalid product type option. Use 0 or 1")
-    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.")
@@ -70,7 +61,7 @@ def download_files(bucket_number, 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}/"
+            s3cmd_command = f"s3cmd get s3://{bucket_number}-{hash}/{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)]
@@ -84,44 +75,48 @@ def download_files(bucket_number, filenames_to_download):
         else:
             print(f"File '{filename}.csv' already exists locally. Skipping download.")
 
+def decompress_file(compressed_file, output_directory):
+    base_name = os.path.splitext(os.path.basename(compressed_file))[0]
 
-def visualize_data(data, output_directory):
+    with open(compressed_file, 'rb') as file:
-    # Extract data for visualization (replace this with your actual data extraction)
+        compressed_data = file.read()
-    x_values = [int(entry[0]) for entry in data]
-    y_values = [float(entry[1]) for entry in data]
 
-    # Plotting
+    # Decode the base64 encoded content
-    plt.plot(x_values, y_values, marker='o', linestyle='-', color='b')
+    decoded_data = base64.b64decode(compressed_data)
-    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
 
+    zip_path = os.path.join(output_directory, 'temp.zip')
+    with open(zip_path, 'wb') as zip_file:
+        zip_file.write(decoded_data)
+
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(output_directory)
+
+    # Rename the extracted data.csv file to the original timestamp-based name
+    extracted_csv_path = os.path.join(output_directory, 'data.csv')
+    if os.path.exists(extracted_csv_path):
+        new_csv_path = os.path.join(output_directory, f"{base_name}.csv")
+        os.rename(extracted_csv_path, new_csv_path)
+
+    os.remove(zip_path)
+    #os.remove(compressed_file)
+    print(f"Decompressed and renamed '{compressed_file}' to '{new_csv_path}'.")
 
-    # 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, product_type):
-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 os.path.exists(output_directory):
+        shutil.rmtree(output_directory)
+
     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)
 
@@ -129,12 +124,17 @@ def download_and_process_files(bucket_number, start_timestamp, end_timestamp, sa
         print("Files already exist in the local folder. Skipping download.")
     else:
         if files_to_download:
-            download_files(bucket_number, files_to_download)
+            download_files(bucket_number, files_to_download, product_type)
 
+    # Decompress all downloaded .csv files (which are actually compressed)
+    compressed_files = [os.path.join(output_directory, file) for file in os.listdir(output_directory) if file.endswith('.csv')]
+    for compressed_file in compressed_files:
+        decompress_file(compressed_file, output_directory)
 
-    # 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)
@@ -171,26 +171,21 @@ def download_and_process_files(bucket_number, start_timestamp, end_timestamp, sa
     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')
+    parser.add_argument('--product_type', required=True, help='Use 0 for Salimax and 1 for Salidomo')
 
-
+    args = parser.parse_args()
-    args = parser.parse_args();
     start_timestamp = args.start_timestamp
     end_timestamp = args.end_timestamp
     keys = args.keys
@@ -198,15 +193,12 @@ def main():
     sampling_stepsize = args.sampling_stepsize
     booleans_as_numbers = args.booleans_as_numbers
     exact_match = args.exact_match
+    product_type = int(args.product_type)
 
-
-    
-    # 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)
+    download_and_process_files(bucket_number, start_timestamp, end_timestamp, sampling_stepsize, keys, booleans_as_numbers, exact_match, product_type)
 
 if __name__ == "__main__":
     main()
-