Merge pull request #9 from nuluh/feature/automate-csv-file

Closes #4

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Python Debugger: Current File with Arguments",
+      "type": "debugpy",
+      "request": "launch",
+      "program": "${file}",
+      "console": "integratedTerminal",
+      "args": ["data/raw", "data/raw"]
+    }
+  ]
+}

code/notebooks/03_feature_extraction.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -186,12 +186,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Print Time-domain Features"
+    "### Print Time-domain Features (Single Mockup Data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
        "0  2.067638  1.917716  0.412307  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,10 +272,12 @@
    "source": [
     "import pandas as pd\n",
     "import sys\n",
+    "import os\n",
     "# Assuming the src directory is one level up from the notebooks directory\n",
     "sys.path.append('../src/features')\n",
     "from time_domain_features import FeatureExtractor\n",
     "\n",
+    "\n",
     "# Extract features\n",
     "extracted = FeatureExtractor(mock_df['SampleData'])\n",
     "\n",
@@ -283,6 +285,85 @@
     "features = pd.DataFrame(extracted.features, index=[0])\n",
     "features\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print Time-domain Features (Multiple CSV Mockup Data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sys\n",
+    "import os\n",
+    "# Assuming the src directory is one level up from the notebooks directory\n",
+    "sys.path.append('../src/features')\n",
+    "from time_domain_features import ExtractTimeFeatures # use wrapper function instead of class for easy use\n",
+    "\n",
+    "def build_features(input_dir):\n",
+    "    all_features = []\n",
+    "    for nth_damage in os.listdir(input_dir):\n",
+    "        nth_damage_path = os.path.join(input_dir, nth_damage)\n",
+    "        if os.path.isdir(nth_damage_path):\n",
+    "            # print(nth_damage)\n",
+    "            for nth_test in os.listdir(nth_damage_path):\n",
+    "                nth_test_path = os.path.join(nth_damage_path, nth_test)\n",
+    "                # print(nth_test_path)\n",
+    "                features = ExtractTimeFeatures(nth_test_path) # return the one csv file feature in dictionary {}\n",
+    "                all_features.append(features)\n",
+    "\n",
+    "    # Create a DataFrame from the list of dictionaries\n",
+    "    df = pd.DataFrame(all_features)\n",
+    "    return df\n",
+    "\n",
+    "data_dir = \"../../data/raw\"\n",
+    "# Extract features\n",
+    "df = build_features(data_dir)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 50 entries, 0 to 49\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   Mean                50 non-null     float64\n",
+      " 1   Max                 50 non-null     float64\n",
+      " 2   Peak (Pm)           50 non-null     float64\n",
+      " 3   Peak-to-Peak (Pk)   50 non-null     float64\n",
+      " 4   RMS                 50 non-null     float64\n",
+      " 5   Variance            50 non-null     float64\n",
+      " 6   Standard Deviation  50 non-null     float64\n",
+      " 7   Power               50 non-null     float64\n",
+      " 8   Crest Factor        50 non-null     float64\n",
+      " 9   Form Factor         50 non-null     float64\n",
+      " 10  Pulse Indicator     50 non-null     float64\n",
+      " 11  Margin              50 non-null     float64\n",
+      " 12  Kurtosis            50 non-null     float64\n",
+      " 13  Skewness            50 non-null     float64\n",
+      "dtypes: float64(14)\n",
+      "memory usage: 5.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
   }
  ],
  "metadata": {

code/src/features/build_features.py

@@ -1,16 +1,39 @@
 # src/features/build_features.py
 import pandas as pd
-from time_domain_features import FeatureExtractor
+from time_domain_features import ExtractTimeFeatures
-import numpy as np
+import os
+import re
 
-def build_features(input_file, output_file):
+# define function, regex pattern for extracting the damage level and test number store in pairs array
-    data = pd.read_csv(input_file)
+def extract_numbers(filename):
-    # Assuming the relevant data is in the first column
+    # Find all occurrences of one or more digits in the filename
-    extractor = FeatureExtractor(data.iloc[:, 0].values)
+    numbers = re.findall(r'\d+', filename)
-    features = extractor.features
+    # Convert the list of number strings to integers
+    numbers = [int(num) for num in numbers]
+    # Convert to a tuple and return
+    return print(tuple(numbers))
 
+def build_features(input_dir, output_dir):
+    all_features = []
+    for nth_damage in os.listdir(input_dir):
+        nth_damage_path = os.path.join(input_dir, nth_damage)
+        if os.path.isdir(nth_damage_path):
+            print(nth_damage)
+            for nth_test in os.listdir(nth_damage_path):
+                nth_test_path = os.path.join(nth_damage_path, nth_test)
+                # print(nth_test_path)
+                features = ExtractTimeFeatures(nth_test_path) # return the one csv file feature in dictionary {}
+                all_features.append(features)
+
+    # Create a DataFrame from the list of dictionaries
+    df = pd.DataFrame(all_features)
+    print(df)
+    # Save the DataFrame to a CSV file in the output directory
+    output_file_path = os.path.join(output_dir, 'combined_features.csv')
+    df.to_csv(output_file_path, index=False)
+    print(f"Features saved to {output_file_path}")
     # Save features to a file
-    np.savez(output_file, **features)
+    # np.savez(output_file, **features)
 
 if __name__ == "__main__":
     import sys
@@ -18,4 +41,4 @@ if __name__ == "__main__":
     output_path = sys.argv[2]  # 'data/features/feature_matrix.npz'
     
     # Assuming only one file for simplicity; adapt as needed
-    build_features(f"{input_path}processed_data.csv", output_path)
+    build_features(input_path, output_path)

code/src/features/time_domain_features.py

@@ -36,6 +36,13 @@ class FeatureExtractor:
             result += f"{feature}: {value:.4f}\n"
         return result
 
+def ExtractTimeFeatures(object):
+    data = pd.read_csv(object, skiprows=1) # Skip the header row separator char info
+    extractor = FeatureExtractor(data.iloc[:, 1].values) # Assuming the data is in the second column
+    features = extractor.features
+    return features
+    # Save features to a file
+    # np.savez(output_file, **features)
 # Usage
 # Assume you have a CSV file with numerical data in the first column
 # Create an instance of the class and pass the path to your CSV file