Merge pull request #118 from biomarkersParkinson/update_print_tutoria…

…l_hr

Update print tutorial hr

docs/tutorials/gait_analysis.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Gait analysis\n",
-    "This tutorial showcases the high-level functions composing the gait pipeline. Before following along, make sure all data preparation steps have been followed in the [data preparation tutorial](data_preparation.ipynb). \n",
+    "This tutorial showcases the high-level functions composing the gait pipeline. Before following along, make sure all data preparation steps have been followed in the data preparation tutorial. \n",
     "\n",
     "To run the complete gait pipeline, a prerequisite is to have both accelerometer and gyroscope data, although a small part of the pipeline requires only accelerometer data. Roughly, the pipeline can be split into seven segments:\n",
     "1. Data preprocessing\n",
@@ -439,7 +439,7 @@
     "- Extract spectral features\n",
     "- Combine both temporal and spectral features into a final dataframe\n",
     "\n",
-    "These steps are encapsulated in `extract_gait_features` (documentation can be found [here](https://github.com/biomarkersParkinson/paradigma/blob/main/src/paradigma/gait/gait_analysis.py))."
+    "These steps are encapsulated in `extract_gait_features` (documentation can be found [here](https://github.com/biomarkersParkinson/paradigma/blob/main/src/paradigma/pipelines/gait_pipeline.py))."
    ]
   },
   {
@@ -836,7 +836,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once again, the `time` column indicates the start time of the window. Therefore, it can be observed that probabilities are predicted of overlapping windows, and not of individual timestamps. The function [`merge_timestamps_with_predictions`](https://github.com/biomarkersParkinson/paradigma/blob/main/src/paradigma/util.py#L382) can be used to retrieve predicted probabilities per timestamp by aggregating the predicted probabilities of overlapping windows. This function is included in the next step."
+    "Once again, the `time` column indicates the start time of the window. Therefore, it can be observed that probabilities are predicted of overlapping windows, and not of individual timestamps. The function [`merge_timestamps_with_predictions`](https://github.com/biomarkersParkinson/paradigma/blob/main/src/paradigma/util.py) can be used to retrieve predicted probabilities per timestamp by aggregating the predicted probabilities of overlapping windows. This function is included in the next step."
    ]
   },
   {