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main.py

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+"""Final RestAPI File"""
+from io import BytesIO
+from datetime import datetime
+import pandas as pd
+from typing import List
+
+import fastapi
+from fastapi import FastAPI, File, UploadFile, Body, Form
+from fastapi.responses import StreamingResponse, Response
+
+import app.src
+from app.src.conversion import h5_to_pandas, csv_to_pandas
+from app.src.ecg_processing import process_batch
+from app.src.pydantic_models import ECGBatch, ECGSample, ECGConfig
+from app.src.configs import OutputFormats
+
+from app.src.logger import setup_logger
+logger = setup_logger(__name__)
+
+# Set metadata
+with open("app/docs/description.md", "r", encoding="utf-8") as f:
+    description = f.read()
+
+tags_metadata = [
+    {
+        "name": "💾conversion",
+        "description": "Convert and create data files without HRV feature processing.",
+    },
+    {
+        "name": "🚀feature processing",
+        "description": "Run HRV feature processing.",
+        "externalDocs": {
+            "description": "Input Data Form external docs",
+            "url": "https://github.com/hubii-world/pipeline_hrv-02#input-data-form",
+        },
+    },
+]
+
+# Initialize an instance of FastAPI
+app = FastAPI(
+    default_response_class=fastapi.responses.ORJSONResponse,
+    openapi_tags=tags_metadata,
+    title="hrv-pipeline-02 💓",
+    description=description,
+    version="0.0.1",
+    contact={
+        "name": "The Open HUman BIosignal Intelligence Platform (HUBII)",
+        "url": "https://hubii.world/hrv-pipeline-02/",
+    })
+
+
+@app.post("/raw_json_input/", tags=["🚀feature processing"], summary="📥 Run feature processing given a raw json input.")
+def process_features_by_raw_json_input(data: ECGBatch = Body(...)):
+    try:
+        samples = data.samples
+        configs = data.configs
+
+        features_df = process_batch(samples, configs)
+        features_dict = features_df.to_dict(orient='records')
+
+        return {
+            "supervisor": data.supervisor,
+            "record_date": data.record_date,
+            "configs": configs,
+            "features": features_dict}
+
+    except Exception as e:
+        error_message = str(e)
+        return {"error": error_message}
+
+
+@app.post("/h5_input/", tags=["🚀feature processing"], summary="📂 Run feature processing given multiple h5 files.")
+def process_features_by_h5_file_input(
+        output_format: OutputFormats = Form(..., alias="Output Format",
+                                            description="Output file format ('csv' or 'json' or 'excel_spreadsheet')."),
+        supervisor: str = Form(..., alias="Supervisor", description="Name of the supervisor doing the analysis."),
+        configs: ECGConfig = Form(None, alias="Additional Configurations",
+                                  description="Additional configurations that should be included."),
+        subject_ids: List[str] = Form(..., alias="Subject ID", description="Id of the subject of the sample data"),
+        ecg_files: List[UploadFile] = File(..., alias="ECG Data", description="HDF5 file with the ecg data."),
+        labels: List[str] = Form(None, alias="Labels", description="List with the label data."),
+):
+    try:
+        logger.info(f"Received {len(ecg_files)} ECG file(s)...")
+        logger.info("Validating inputs...")
+        assert len(labels) in [0, len(ecg_files)], "Not enough labels defined, none or one for each sample."
+        assert len(subject_ids) <= len(ecg_files), "Too many subject IDs defined, maximal one for each sample."
+        if len(subject_ids) == 1:
+            subject_ids = [subject_ids[0]] * len(ecg_files)
+        if len(subject_ids) != len(ecg_files):
+            subject_ids += ["unknown"] * (len(ecg_files) - len(subject_ids))
+
+        logger.info("Extracting samples from files...")
+        samples = []
+        for i, file in enumerate(ecg_files):
+            sample_df = h5_to_pandas(file.file)
+            freq = int(sample_df["frequency"].iloc[0])
+            device_name = str(sample_df["device_name"].iloc[0])
+
+            samples.append(
+                ECGSample(
+                    subject_id=subject_ids[i],
+                    frequency=freq,
+                    device_name=device_name,
+                    timestamp_idx=sample_df["timestamp_idx"].tolist(),
+                    ecg=sample_df["ecg"].tolist(),
+                    label=labels[i] if labels else None
+                )
+            )
+
+        logger.info("Processing batch of samples...")
+        features_df = process_batch(samples, configs)
+
+        if output_format == "json":
+            features_dict = features_df.to_dict(orient='records')
+            # Return JSON response
+            return {
+                "supervisor": supervisor,
+                "record_date": datetime.now(),
+                "configs": configs,
+                "features": features_dict
+            }
+        elif output_format == "csv":
+            # Return CSV file
+            csv_data = features_df.to_csv(index=False)
+            filename = "features_output.csv"
+            return StreamingResponse(iter([csv_data]), media_type='text/csv',
+                                     headers={'Content-Disposition': f'attachment; filename="{filename}"'})
+        elif output_format == "excel_spreadsheet":
+            # Return Excel file
+            output_buffer = BytesIO()
+            with pd.ExcelWriter(output_buffer, engine='xlsxwriter') as writer:
+                features_df.to_excel(writer, index=False, sheet_name='Sheet1')
+            output_buffer.seek(0)
+            response = Response(content=output_buffer.getvalue(),
+                                media_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')
+            response.headers['Content-Disposition'] = 'attachment; filename="features_output.xlsx"'
+            return response
+        else:
+            raise ValueError(f"Output format '{output_format}' not supported.")
+
+    except Exception as e:
+        error_message = str(e)
+        return {"error": error_message}
+
+
+@app.post("/csv_input/", tags=["🚀feature processing"], summary="📂 Run feature processing given multiple csv files.")
+def process_features_by_csv_file_input(
+        output_format: OutputFormats = Form(..., alias="Output Format",
+                                            description="Output file format ('csv' or 'json' or 'excel_spreadsheet')."),
+        csv_file: UploadFile = File(..., alias="CSV Data", description="CSV file with the ecg data."),
+):
+    try:
+        # Read csv file
+        df = csv_to_pandas(csv_file.file)
+        # Implode
+        cols_to_implode = ['timestamp_idx', 'ecg', 'label']
+        df_imploded = df.groupby(list(set(df.columns) - set(cols_to_implode))) \
+            .agg({'timestamp_idx': list,
+                  'ecg': list,
+                  'label': list}) \
+            .reset_index()
+        # Get metadata
+        config_cols = [col for col in df.columns if col.startswith('configs.')]
+        configs = df_imploded[config_cols].iloc[0].to_dict()
+        configs = {key.removeprefix('configs.'): value for key, value in configs.items()}
+        configs = ECGConfig(**configs)
+        batch_cols = [col for col in df.columns if col.startswith('batch.')]
+        batch = df_imploded[batch_cols].iloc[0].to_dict()
+        batch = {key.removeprefix('batch.'): value for key, value in batch.items()}
+        # Get samples
+        samples = df_imploded.to_dict(orient='records')
+        samples = [ECGSample(**sample) for sample in samples]
+
+        logger.info("Processing batch of samples...")
+        features_df = process_batch(samples, configs)
+
+        if output_format == "json":
+            features_dict = features_df.to_dict(orient='records')
+            # Return JSON response
+            return {
+                "supervisor": batch['supervisor'],
+                "record_date": batch['record_date'],
+                "configs": configs,
+                "features": features_dict
+            }
+        elif output_format == "csv":
+            # Return CSV file
+            csv_data = features_df.to_csv(index=False)
+            filename = "features_output.csv"
+            return StreamingResponse(iter([csv_data]), media_type='text/csv',
+                                     headers={'Content-Disposition': f'attachment; filename="{filename}"'})
+        elif output_format == "excel_spreadsheet":
+            output_buffer = BytesIO()
+            with pd.ExcelWriter(output_buffer, engine='xlsxwriter') as writer:
+                features_df.to_excel(writer, index=False, sheet_name='Sheet1')
+            output_buffer.seek(0)
+            response = Response(content=output_buffer.getvalue(),
+                                media_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')
+            response.headers['Content-Disposition'] = 'attachment; filename="features_output.xlsx"'
+            return response
+        else:
+            raise ValueError(f"Output format '{output_format}' not supported.")
+
+    except Exception as e:
+        error_message = str(e)
+        return {"error": error_message}

requirements.txt

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+#
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+#    pip-compile requirements.in
+#
+annotated-types==0.6.0
+    # via pydantic
+anyio==3.7.1
+    # via
+    #   fastapi
+    #   starlette
+certifi==2023.11.17
+    # via requests
+charset-normalizer==3.3.2
+    # via requests
+click==8.1.7
+    # via uvicorn
+colorama==0.4.6
+    # via
+    #   click
+    #   colorlog
+colorlog==6.8.0
+    # via -r requirements.in
+contourpy==1.2.0
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+exceptiongroup==1.2.0
+    # via anyio
+fastapi==0.104.1
+    # via -r requirements.in
+fonttools==4.46.0
+    # via matplotlib
+h11==0.14.0
+    # via uvicorn
+h5py==3.10.0
+    # via -r requirements.in
+idna==3.6
+    # via
+    #   anyio
+    #   requests
+joblib==1.3.2
+    # via scikit-learn
+kiwisolver==1.4.5
+    # via matplotlib
+matplotlib==3.8.2
+    # via neurokit2
+neurokit2==0.2.7
+    # via -r requirements.in
+numpy==1.26.2
+    # via
+    #   contourpy
+    #   h5py
+    #   matplotlib
+    #   neurokit2
+    #   pandas
+    #   scikit-learn
+    #   scipy
+packaging==23.2
+    # via matplotlib
+pandas==2.1.3
+    # via
+    #   -r requirements.in
+    #   neurokit2
+pillow==10.1.0
+    # via matplotlib
+pydantic==2.5.2
+    # via
+    #   -r requirements.in
+    #   fastapi
+pydantic-core==2.14.5
+    # via pydantic
+pyparsing==3.1.1
+    # via matplotlib
+python-dateutil==2.8.2
+    # via
+    #   matplotlib
+    #   pandas
+python-multipart==0.0.6
+    # via -r requirements.in
+pytz==2023.3.post1
+    # via pandas
+requests==2.31.0
+    # via -r requirements.in
+scikit-learn==1.3.2
+    # via
+    #   -r requirements.in
+    #   neurokit2
+scipy==1.11.4
+    # via
+    #   -r requirements.in
+    #   neurokit2
+    #   scikit-learn
+six==1.16.0
+    # via python-dateutil
+sniffio==1.3.0
+    # via anyio
+starlette==0.27.0
+    # via fastapi
+threadpoolctl==3.2.0
+    # via scikit-learn
+typing-extensions==4.8.0
+    # via
+    #   fastapi
+    #   pydantic
+    #   pydantic-core
+    #   uvicorn
+tzdata==2023.3
+    # via pandas
+urllib3==2.1.0
+    # via requests
+uuid==1.30
+    # via -r requirements.in
+uvicorn==0.24.0.post1
+    # via -r requirements.in