update: remove todos (create a new branch)

Author: mayabflannery

src/postprocess/scripts/postprocessing_template.ipynb

@@ -93,9 +93,7 @@
    "outputs": [],
    "source": [
     "files_watch_summary = data_access.summarise_files_in_directory(cfg)\n",
-    "files_watch_summary\n",
-    "\n",
-    "# TODO: fix this warning []"
+    "files_watch_summary"
    ]
   },
   {
@@ -183,14 +181,6 @@
     "raw_data_full.head()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "627b216b-03c1-4e05-a2d9-619710b35445",
-   "metadata": {},
-   "source": [
-    "Note: The first value in the `timeDifference` column is `NaN` because [TODO:]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "312181a7-80e7-4e04-a617-cae3f6d803c7",
@@ -233,68 +223,14 @@
     "## Visualization"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d41ffd18-652e-4e98-921c-5753aca89ce3",
-   "metadata": {},
-   "source": [
-    "Coarse overview of time-series\n",
-    "TODO"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "cc0e2a43-2484-4c57-9dc7-87f3102d1ea7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "reporting.plotly_data(raw_data_trimmed, cfg)\n",
-    "\n",
-    "#TODO 4 second bins (efficiency)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4d13fd7b-a6c0-4009-a0d2-0cd497b4be5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Datashader\n",
-    "import xarray as xr\n",
-    "import datashader as ds\n",
-    "import datashader.transfer_functions as tf\n",
-    "\n",
-    "\n",
-    "def get_ds_aggs(df, name_y, h=1500, w=4000):\n",
-    "    # datashader does not have native date support -- convert time to int\n",
-    "    d = df.copy()\n",
-    "    d.loc[:, \"ITime\"] = d[\"time\"].astype(\"int64\")\n",
-    "    d.sort_values(\"ITime\", inplace=True)\n",
-    "    dx_min = d[\"ITime\"].min()  # For manual scaling of img\n",
-    "    dx_max = d[\"ITime\"].max()\n",
-    "    dy_min = d[name_y].min() - 2\n",
-    "    dy_max = d[name_y].max() + 2\n",
-    "\n",
-    "    # For multiple plots (each HR timeseries), cvs must be same size\n",
-    "    cvs = ds.Canvas(\n",
-    "        x_range=(dx_min, dx_max), y_range=(dy_min, dy_max), plot_height=h, plot_width=w\n",
-    "    )\n",
-    "    aggs = {}\n",
-    "    for c in d[\"watchId\"].unique():\n",
-    "        aggs[c] = cvs.line(d[d[\"watchId\"] == c], \"ITime\", name_y)\n",
-    "    return aggs\n",
-    "\n",
-    "\n",
-    "def plot_raw_individual_watches(df, config_dat, value=\"heartRate\"):\n",
-    "    dir_fig_out = config_dat[\"directories\"][\"figures\"]\n",
-    "    df_agg = get_ds_aggs(df, value)\n",
-    "    for w in df_agg.keys():\n",
-    "        img = tf.shade(df_agg[w])\n",
-    "        ds.utils.export_image(img, dir_fig_out + f\"fig_raw_{value}_{w}\")\n",
-    "\n",
-    "plot_raw_individual_watches(raw_data_trimmed, cfg, value=\"ppgRaw\")"
+    "reporting.plotly_data(raw_data_trimmed, cfg)"
    ]
   },
   {
@@ -353,8 +289,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "resampled_data_HR_1000ms.reset_index('time', inplace=True)\n",
-    "# TODO: fix plotting for resampled df\n"
+    "resampled_data_HR_1000ms.reset_index('time', inplace=True)"
    ]
   },
   {
@@ -366,127 +301,6 @@
    "source": [
     "reporting.plotly_data(resampled_data_HR_1000ms, cfg)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "641e0d26-9d39-4193-b171-c02594c5b1e4",
-   "metadata": {},
-   "source": [
-    "## PPG processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a1499ff5-7484-420d-a066-df4f5f4ce386",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2f8516d3-16a6-4709-8f45-259e92292814",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_data_trimmed.reset_index(inplace=True) # TODO: Fix in resample_PPG()\n",
-    "\n",
-    "resampled_data_PPG_25ms = pipelines.resample_PPG(\n",
-    "    raw_data_trimmed, cfg, save_data=True\n",
-    ")\n",
-    "# TODO: Fix warning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9438a7c7-d6b4-4edf-b043-9e85152496f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resampled_data_PPG_25ms\n",
-    "\n",
-    "# Plot output -- compare to original signal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b2d351a-eddd-4b9b-99dd-fe42f06cedbb",
-   "metadata": {},
-   "source": [
-    "## Peak finding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "443249d5-bb91-4a29-af75-ecbf77d1fe2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PPG_peaks = pipelines.PPG_find_peaks(resampled_data_PPG_25ms, cfg)\n",
-    "PPG_peaks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c5b1cd2-fd19-4486-ace3-e693362c101d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc5c7797-36fd-44b6-ac4d-c785281ac712",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PPG_peaks['PPG_PeaksAlt'] = PPG_peaks['PPG_Peaks'] * PPG_peaks['PPG_Clean'].std() * 3\n",
-    "PPG_peaks.loc[PPG_peaks['watchId'] == 'W020', ['PPG_Clean', 'PPG_PeaksAlt']].plot(figsize=(1000,5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6d60aac-c751-41bf-99ee-d83a1ba65afd",
-   "metadata": {},
-   "source": [
-    "TODO:\n",
-    "\n",
-    "Plot comparison of PPG HR and heartRate (from bangle)\n",
-    "\n",
-    "more meaningful function names\n",
-    "\n",
-    "consistent index axis names\n",
-    "\n",
-    "rename plot_raw_data? generalize to any data? also handle resampling.\n",
-    "\n",
-    "final data frame PPG-based HRV"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7c75b73-6ae5-4b59-af15-8a2b92406121",
-   "metadata": {},
-   "source": [
-    "# More"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "608562f5-85c4-461a-bf70-b0c8172276a4",
-   "metadata": {},
-   "source": [
-    "## Read processed data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "560ed64e-8bd7-4b40-bdce-6690ecdb8b1f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {