Tutorial ppg (#106)

* Start heart rate estimation including tfd

* stash updates

* update based on merge conflicts

* update hr main function

* update minor stuff matlab files

* change tfd parameters to dict

* First steps to refactoring to numpy

* Finalizing heart rate pipeline plus renaming preprocessing to acc instead of imu

* setting execution count to null

* fix output types

* fixing pytype due to old configs

* fix min_hr_samples pytype error

* remove irrelevant config

* resolved comments PR reviewer

* remove data type since it causes pytype error

* clear execution count

* import heart_rate_estimation

* merge updates

* update ppg_analysis

* replace df to numpy arrays

* fix notebook

* adding accelerometer feature

* fix type error list vs float

* implement accelerometer feature

* update matlab file based on refactoring

* fixing types, docstrings and readibility

* adding documentation acc feature

* add documentation of autocorrelation

* Made requested changes

* fix config

* adding aggregation heart rate

* update aggregates

* remove config

* add start tutorial

* aggregation implementation with dictionary

* add lines for consistency

* moved windoweddataextractor to segmenting

* resolve merge conflict

* update loading + preprocessing with seperate functionality

* update tutorial notebook and corresponding notebook

* Update test to io function

* update tutorial and notebook with simplified loading of the data

* Reset execution counts in notebook cells

* update execution_counts and output

* Updates from PR feedback

* fix type error

* made final feedback

---------

Co-authored-by: Erikpostt <erik_post_6@hotmail.com>
Co-authored-by: Erik Post <57133568+Erikpostt@users.noreply.github.com>

docs/notebooks/ppg/ppg_analysis.ipynb

@@ -13,10 +13,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
+    "import importlib.resources\n",
+    "import pickle\n",
+    "from pathlib import Path\n",
+    "\n",
     "from paradigma.config import PPGConfig, IMUConfig, SignalQualityFeatureExtractionConfig, SignalQualityFeatureExtractionAccConfig, SignalQualityClassificationConfig, HeartRateExtractionConfig\n",
-    "from paradigma.preprocessing import scan_and_sync_segments, preprocess_ppg_data\n",
-    "from paradigma.heart_rate.heart_rate_analysis import extract_signal_quality_features, signal_quality_classification, estimate_heart_rate, aggregate_heart_rate"
+    "from paradigma.preprocessing import preprocess_ppg_data\n",
+    "from paradigma.heart_rate.heart_rate_analysis import extract_signal_quality_features, signal_quality_classification, estimate_heart_rate, aggregate_heart_rate\n",
+    "from paradigma.util import load_tsdf_dataframe"
    ]
   },
   {
@@ -33,18 +37,42 @@
     "branch = 'heart_rate'\n",
     "sensor = 'ppg'\n",
     "\n",
-    "path_to_data =  '../../../tests/data'\n",
-    "path_to_classifier = os.path.join(path_to_data, '0.classification', sensor)\n",
-    "path_to_sensor_data = os.path.join(path_to_data, '1.prepared_data')\n",
-    "path_to_preprocessed_data = os.path.join(path_to_data, '2.preprocessed_data', sensor)\n",
-    "path_to_quality_features = os.path.join(path_to_data, '3.extracted_features', branch)\n",
-    "path_to_signal_quality = os.path.join(path_to_data, '4.predictions', branch)\n",
-    "path_to_hr_estimate = os.path.join(path_to_data, '5.quantification', branch)\n",
-    "path_to_hr_aggregation = os.path.join(path_to_data, '6.aggregation', branch)\n",
+    "ppg_prefix = 'PPG'\n",
+    "imu_prefix = 'IMU'\n",
+    "\n",
+    "ppg_classifier_package_filename = 'ppg_quality_classifier.pkl'\n",
+    "\n",
+    "path_to_data =  Path('../../../tests/data')\n",
+    "path_to_assets = Path('../../../src/paradigma/assets')\n",
+    "\n",
+    "path_to_classifier = path_to_data / '0.Classification' / sensor\n",
+    "path_to_prepared_data = path_to_data / '1.prepared_data'\n",
+    "path_to_preprocessed_data = path_to_data / '2.preprocessed_data' / sensor\n",
+    "path_to_extracted_features = path_to_data / '3.extracted_features' / branch\n",
+    "path_to_predictions = path_to_data / '4.predictions' / branch\n",
+    "path_to_quantification = path_to_data / '5.quantification' / branch\n",
+    "path_to_aggregation = path_to_data / '6.aggregation' / branch\n",
     "\n",
     "aggregation_filename = 'heart_rate_aggregates.json'"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ppg, _, _ = load_tsdf_dataframe(path_to_prepared_data / ppg_prefix, prefix = ppg_prefix)\n",
+    "df_imu, _, _ = load_tsdf_dataframe(path_to_prepared_data / imu_prefix, prefix = imu_prefix)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -61,8 +89,7 @@
     "ppg_config = PPGConfig()\n",
     "imu_config = IMUConfig()\n",
     "\n",
-    "metadatas_ppg, metadatas_imu = scan_and_sync_segments(os.path.join(path_to_sensor_data, sensor), os.path.join(path_to_sensor_data, 'imu'))\n",
-    "df_ppg_proc, df_acc_proc = preprocess_ppg_data(metadatas_ppg[0], metadatas_imu[0], path_to_preprocessed_data, ppg_config, imu_config)"
+    "df_ppg_proc, df_acc_proc = preprocess_ppg_data(df_ppg, df_imu, ppg_config, imu_config)"
    ]
   },
   {
@@ -96,8 +123,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "full_path_to_classifier_package = path_to_assets / ppg_classifier_package_filename\n",
+    "with importlib.resources.files('paradigma.assets').joinpath(ppg_classifier_package_filename).open('rb') as f:\n",
+    "    clf_package = pickle.load(f)\n",
     "config = SignalQualityClassificationConfig()\n",
-    "df_sqa = signal_quality_classification(df_features, config, path_to_classifier)"
+    "df_sqa = signal_quality_classification(df_features, config, clf_package)"
    ]
   },
   {